{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Latent Variable Models and Variational Bayes" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "### Preliminaries\n", "\n", "- Goal \n", " - Introduction to latent variable models and variational inference by Free energy minimization \n", "- Materials\n", " - Mandatory\n", " - These lecture notes\n", " - Optional \n", " - Bishop (2016), pp. 461-486 (sections 10.1, 10.2 and 10.3) \n", " - Ariel Caticha (2010), [Entropic Inference](https://arxiv.org/abs/1011.0723)\n", " - tutorial on entropic inference, which is a generalization to Bayes rule and provides a foundation for variational inference.\n", " - references \n", " - Blei et al. (2017), [Variational Inference: A Review for Statisticians](https://www.tandfonline.com/doi/full/10.1080/01621459.2017.1285773) \n", " - Lanczos (1961), [The variational principles of mechanics](https://www.amazon.com/Variational-Principles-Mechanics-Dover-Physics/dp/0486650677)\n", " - Senoz et al. (2021), [Variational Message Passing and Local Constraint Manipulation in Factor Graphs](https://www.mdpi.com/1099-4300/23/7/807)\n", " - Dauwels (2007), [On variational message passing on factor graphs](https://github.com/bertdv/BMLIP/blob/master/lessons/notebooks/files/Dauwels-2007-on-variational-message-passing-on-factor-graphs.pdf)\n", " - Shore and Johnson (1980), [Axiomatic Derivation of the Principle of Maximum Entropy and the Principle of Minimum Cross-Entropy](https://github.com/bertdv/BMLIP/blob/master/lessons/notebooks/files/ShoreJohnson-1980-Axiomatic-Derivation-of-the-Principle-of-Maximum-Entropy.pdf)\n", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Challenge : Density Modeling for the Old Faithful Data Set\n", "\n", "- You're now asked to build a density model for a data set ([Old Faithful](https://en.wikipedia.org/wiki/Old_Faithful), Bishop pg. 681) that clearly is not distributed as a single Gaussian:\n", "\n", "

" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Unobserved Classes\n", "\n", "- Consider again a set of observed data $D=\\{x_1,\\dotsc,x_N\\}$\n", "\n", "- This time we suspect that there are _unobserved_ class labels that would help explain (or predict) the data, e.g.,\n", " - the observed data are the color of living things; the unobserved classes are animals and plants.\n", " - observed are wheel sizes; unobserved categories are trucks and personal cars.\n", " - observed is an audio signal; unobserved classes include speech, music, traffic noise, etc.\n", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " \n", "- Classification problems with unobserved classes are called **Clustering** problems. The learning algorithm needs to **discover the classes from the observed data**." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### The Gaussian Mixture Model\n", "\n", "- The spread of the data in the illustrative example looks like it could be modeled by two Gaussians. Let's develop a model for this data set. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Let $D=\\{x_n\\}$ be a set of observations. We associate a one-hot coded hidden class label $z_n$ with each observation:\n", "\n", "$$\\begin{equation*}\n", "z_{nk} = \\begin{cases} 1 & \\text{if } x_n \\in \\mathcal{C}_k \\text{ (the $k$-th class)}\\\\\n", " 0 & \\text{otherwise} \\end{cases}\n", "\\end{equation*}$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- We consider the same model as we did in the [generative classification lesson](https://nbviewer.jupyter.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Generative-Classification.ipynb#GDA): the data for each class is distributed as a Gaussian:\n", "$$\\begin{align*}\n", "p(x_n | z_{nk}=1) &= \\mathcal{N}\\left( x_n | \\mu_k, \\Sigma_k\\right)\\\\\n", "p(z_{nk}=1) &= \\pi_k\n", "\\end{align*}$$\n", "which can be summarized with the selection variables $z_{nk}$ as\n", "$$\\begin{align*}\n", "p(x_n,z_n) &= \\prod_{k=1}^K (\\underbrace{\\pi_k \\cdot \\mathcal{N}\\left( x_n | \\mu_k, \\Sigma_k\\right) }_{p(x_n,z_{nk}=1)})^{z_{nk}} \n", "\\end{align*}$$\n", "\n", "- *Again*, this is the same model as we defined for the generative classification model: A Gaussian-Categorical model but now with unobserved classes. \n", "\n", "- This model (with **unobserved class labels**) is known as a **Gaussian Mixture Model** (GMM)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### The Marginal Distribution for the GMM\n", "\n", "- In the literature, the GMM is often introduced by the marginal distribution for an _observed_ data point $x_n$, given by\n", "\n", "$$\\begin{align*}{}\n", "p(x_n) &= \\sum_{z_n} p(x_n,z_n) \\\\\n", " &= \\sum_{k=1}^K \\pi_k \\cdot \\mathcal{N}\\left( x_n | \\mu_k, \\Sigma_k \\right) \\tag{B-9.12}\n", "\\end{align*}$$\n", "\n", "- Full proof as an [exercise](https://nbviewer.org/github/bertdv/BMLIP/blob/master/lessons/exercises/Exercises-Latent-Variable-Models-and-VB.ipynb). \n", "\n", "- Eq. B-9.12 reveals the link to the name Gaussian *mixture model*. The priors $\\pi_k$ for the $k$-th class are also called **mixture coefficients**. \n", "\n", "- Be aware that Eq. B-9.12 is not the generative model for the GMM! The generative model is the joint distribution $p(x,z)$ over all variables, including the latent variables. \n", " \n", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### GMM is a very flexible model\n", "\n", "- GMMs are very popular models. They have decent computational properties and are **universal approximators of densities** (as long as there are enough Gaussians of course)\n", "\n", "

\n", "\n", "- (In the above figure, the Gaussian components are shown in red and the pdf of the mixture models in blue)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Latent Variable Models\n", "\n", "- The GMM contains both _observed_ variables $ \\{x_n\\}$, (unobserved) _parameters_ $\\theta= \\{\\pi_k,\\mu_k, \\Sigma_k\\}$ _and_ unobserved (synonym: latent, hidden) variables $\\{z_{nk}\\}$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- From a Bayesian viewpoint, both latent variables $\\{z_{nk}\\}$ and parameters $\\theta$ are just unobserved variables for which we can set a prior and compute a posterior by Bayes rule. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Note that $z_{nk}$ has a subscript $n$, hence its value depends not only on the class ($k$) but also on the $n$-th observation (in contrast to parameters). These observation-dependent latent variables are generally a useful tool to encode additional structure in the model about the causes of your observations. Here (in the GMM), the latent variables $\\{z_{nk}\\}$ encode (unobserved) class membership. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Models with observation-dependent latent variables are generally called **Latent Variable Models**. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- By adding model structure through (equations among) observation-dependent latent variables, we can often build more accurate models for very complex processes. Unfortunately, adding structure through observation-dependent latent variables in models often is accompanied by a more complex inference task." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Inference for GMM is Difficult\n", "\n", "- Indeed, the fact that the observation-dependent class labels are _unobserved_ for the GMM, leads to a problem for processing new data by Bayes rule in a GMM.\n", "\n", "- Consider a given data set $D = \\{x_n\\}$. We recall here the log-likelihood for the Gaussian-Categorial Model, see the [generative classification lesson](https://nbviewer.jupyter.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Generative-Classification.ipynb):\n", "\n", "$$\n", "\\log\\, p(D|\\theta) = \\sum_{n,k} y_{nk} \\underbrace{ \\log\\mathcal{N}(x_n|\\mu_k,\\Sigma) }_{ \\text{Gaussian} } + \\underbrace{ \\sum_{n,k} y_{nk} \\log \\pi_k }_{ \\text{multinomial} } \\,.\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Since the class labels $y_{nk} \\in \\{0,1\\}$ were assumed to be given by the data set, maximization of this expression decomposed into a set of simple update rules for the Gaussian and multinomial distributions. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- However, for the Gaussian mixture model (same log-likelihood function with $z_{nk}$ replacing $y_{nk}$), the class labels $\\{z_{nk}\\}$ are _unobserved_ and they need to be estimated alongside with the parameters." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- There is no known conjugate prior for the latent variables for the GMM likelihood function and, therefore, we cannot compute Bayes rule to get a closed-form expression for the posterior over the latent variables:\n", "$$ \\underbrace{p(\\{z_{nk}\\},\\{\\mu_k,\\Sigma_k,\\pi_k\\} | D)}_{\\text{posterior (no analytical solution)}} \\propto \\underbrace{p(D\\,|\\,\\{z_{nk}\\},\\{\\mu_k,\\Sigma_k,\\pi_k\\})}_{\\text{likelihood}} \\cdot \\underbrace{p( \\{z_{nk}\\},\\{\\mu_k,\\Sigma_k,\\pi_k\\} )}_{\\text{prior (no known conjugate)}} $$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Can we still compute an approximate posterior? In this lesson, we introduce an approximate Bayesian inference method known as **Variational Bayes** (VB) (also known as **Variational Inference**) that can be used for Bayesian inference in models with latent variables. Later in this lesson, we will use VB to do inference in the GMM. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ " ### The Variational Free Energy Functional\n", "\n", " - We'll start from scratch. Consider a model $p(x,z) = p(x|z) p(z)$, where $x$ and $z$ are observed and latent variables respectively. $z$ may include parameters but also observation-dependent latent variables. \n", "\n", " - The goal of Bayesian inference is to transform the (known) _likelihood-times-prior_ factorization of the full model to a _posterior-times-evidence_ decomposition: \n", " $$ \\underbrace{p(x|z) p(z)}_{\\text{what we know}} \\rightarrow \\underbrace{p(z|x) p(x)}_{\\text{what we want}} $$\n", "\n", " - Remember from the [Bayesian machine learning lesson](https://nbviewer.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Bayesian-Machine-Learning.ipynb#Bayesian-model-evidence) that negative log-evidence can be decomposed as \"complexity\" minus \"accuracy\" terms (the CA decomposition):\n", " $$\n", " -\\log p(x) = \\underbrace{ \\int p(z|x) \\log \\frac{p(z|x)}{p(z)} \\mathrm{d}z }_{\\text{complexity}} - \\underbrace{\\int p(z|x) \\log p(x|z) \\mathrm{d}z}_{\\text{accuracy}}\n", " $$\n", "\n", " - The CA decomposition cannot be evaluated because it depends on the posterior $p(z|x)$, which cannot be evaluated since it is the objective of the inference process. \n", "\n", " - Let's now introduce a distribution $q(z)$ that we use to approximate the posterior $p(z|x)$, and assume that $q(z)$ can be evaluated! \n", " \n", " - If will substitute $q(z)$ for $p(z|x)$ in the CA decomposition, then we obtain \n", " $$\n", " F[q] \\triangleq \\underbrace{ \\int q(z) \\log \\frac{q(z)}{p(z)} \\mathrm{d}z }_{\\text{complexity}} - \\underbrace{\\int q(z) \\log p(x|z) \\mathrm{d}z}_{\\text{accuracy}}\n", " $$\n", "\n", " - This expression is called the variational _Free Energy_ (FE). We consider the Free Energy $F$ as a function of the posterior $q(z)$. Technically, a function of a function is called a functional, and we write square brackets (e.g., $F[q]$) to differentiate functionals from functions (e.g., $q(z)$). \n", " \n", " - Note that all factors in the CA decomposition of FE (i.e., $q(z)$, $p(z)$ and $p(x|z)$) can be evaluated as a function of $z$ (and $x$ is observed), and therefore the FE can be evaluated. This is important: log-evidence cannot be evaluated, but FE _can_ be evaluated! \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inference by FE Minimization\n", "\n", "- It turns out that we can do (approximate) Bayesian inference through FE Minimization (FEM) with respect to $q$. \n", "\n", "- To explain inference by FEM, we first rewrite FE in terms of \"inference bound\" minus \"log-evidence\" terms (the bound-evidence (BE) decomposition):\n", "$$\\begin{align*}\n", " F[q] &= \\underbrace{ \\int q(z) \\log \\frac{q(z)}{p(z)} \\mathrm{d}z }_{\\text{complexity}} - \\underbrace{\\int q(z) \\log p(x|z) \\mathrm{d}z}_{\\text{accuracy}} \\\\\n", " &= \\int q(z) \\log \\frac{q(z)}{p(z) p(x|z) }\\mathrm{d}z \\\\\n", " &= \\int q(z) \\log \\frac{q(z)}{p(z|x) p(x)}\\mathrm{d}z \\\\\n", " &= \\underbrace{\\int q(z) \\log \\frac{q(z)}{p(z|x)}\\mathrm{d}z}_{\\text{inference bound}\\geq 0} - \\underbrace{\\log p(x)}_{\\text{log-evidence}} \n", " \\end{align*}$$\n", "\n", "- Note that the inference bound is a [Kullback-Leibler (KL) divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence) between an (approximate) posterior $q(z)$ and the (perfect) Bayesian posterior $p(z|x)$. See this [slide in the BML Class](https://nbviewer.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Bayesian-Machine-Learning.ipynb#KLD) for more info on the KL divergence. \n", "\n", "- Since the second term (log-evidence) does not involve $q(z)$, FEM over $q$ will bring $q(z)$ closer to the Bayesian posterior $p(z|x)$.\n", "\n", "- Since $\\mathrm{KL}[q(z),p(z|x)]\\geq 0$ for any $q(z)$, and $\\mathrm{KL}[q(z),p(z|x)]= 0$ only if $q(z)=p(z|x)$, the FE is always an upperbound on (minus) log-evidence, i.e.,\n", "$$\n", "F[q] \\geq -\\log p(x) \\,.\n", "$$\n", "\n", "- As a result, **global FEM recovers Bayes rule**, i.e., global optimization of FE w.r.t. $q$ leads to\n", "$$q^*(z) = \\arg\\min_q F[q]$$\n", "where\n", "$$\\begin{align*}\n", " \\text{posterior: } q^*(z) &= p(z|x) \\\\\n", " \\text{evidence: } F[q^*] &= -\\log p(x) \n", "\\end{align*}$$\n", "\n", "- In practice, even if we cannot attain the global minimum of FE, we can still use a local minimum $$\\hat{q}(z) \\approx \\arg\\min_q F[q]$$ to accomplish **approximate Bayesian inference** by: \n", "$$\\begin{align*}\n", " \\text{posterior: } \\hat{q}(z) &\\approx p(z|x) \\\\\n", " \\text{evidence: } F[\\hat{q}] &\\approx -\\log p(x)\n", " \\end{align*}$$\n", "\n", "- In short, FE minimization transforms an inference problem (that involves integration) to an optimization problem! Generally, optimization problems are easier to solve than integration problems. \n", "\n", "- Executing inference by minimizing the variational FE functional is called **Variational Bayes** (VB) or variational inference. \n", "\n", "- (As an aside), note that Bishop introduces in Eq. B-10.3 an _Evidence Lower BOund_ (in modern machine learning literature abbreviated as **ELBO**) $\\mathcal{L}[q]$ that equals the _negative_ FE ($\\mathcal{L}[q]=-F[q]$). In this class, we prefer to discuss inference in terms of minimizing Free Energy rather than maximizing ELBO, but note that these two concepts are equivalent. (The reason why we prefer the Free Energy formulation relates to the terminology in the Free Energy Principle, which we introduce in the [Intelligent Agents and active Inference lesson (B12)](https://nbviewer.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Intelligent-Agents-and-Active-Inference.ipynb)). \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Constrained FE Minimization \n", "\n", "- It is common to add simplifying constraints to optimization problems to make a difficult optimization task tractible. This is also common practice when approximating Bayesian inference by FE minimization.\n", "\n", "- There are three important cases of adding constraints to $q(z)$ that often alleviates the FE minimization task:\n", "\n", " 1. #### form constraints\n", " \n", " - For almost every practical setting, we constrain the posterior $q(z)$ to be a specific parameterized probability distribution, e.g., $$q(z) = \\mathcal{N}\\left( z | \\mu, \\Sigma \\right)\\,.$$ \n", " - In this case, the _functional_ minimization problem for $F[q]$ reduces to the minimization of a _function_ \n", " $$F(\\mu,\\Sigma) = \\int \\mathcal{N}\\left( z | \\mu, \\Sigma \\right) \\log \\frac{\\mathcal{N}\\left( z | \\mu, \\Sigma \\right)}{p(x,z)}\\mathrm{d}z$$ \n", " w.r.t. the parameters $\\mu$ and $\\Sigma$. \n", " - We can often use standard gradient-based optimization methods to minimize the FE. \n", " - In the figure below (see Bishop Fig.10.1a, pg.464), an [intractable Bayesian posterior](https://nbviewer.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Discriminative-Classification.ipynb#Laplace-example) \n", " (yellow) for a binary classification problem has been approximated by a Laplace approximation (red) and a variational posterior $q(z) \\sim \\mathcal{N}(\\mu,\\sigma^2)$ (green). \n", " \n", "

\n", "\n", " 2. #### factorization constraints\n", " - In addition to form constraints, it is also common to constrain the posterior $q(z)$ by a specific factorization. For instance, in _mean-field factorization_, we constrain the posterior to factorize into a set of independent factors, i.e., \n", " $$\n", " q(z) = \\prod_{j=1}^m q_j(z_j)\\,, \\tag{B-10.5}\n", " $$ \n", " - Variational inference with mean-field factorization has been worked out in detail as the **Coordinate Ascent Variational Inference** (CAVI) algorithm. See the [Optional Slide on CAVI](#CAVI) for details. \n", " - Mean-field factorization is just an example of various _factorization constraints_ that have been successfully applied to FEM. \n", "\n", " 3. #### other constraints, e.g., the Expectation-Minimization (EM) algorithm\n", " - Aside from form and factorization constraints, several ad hoc algorithms have been derived that ease the process of FE minimization for particular models. \n", " - In particular, the Expectation-Maximization (EM) algorithm is a famous special case of constrained FE minimization. The EM algorithm places some constraints on both the posterior $q(z)$ and the prior $p(z)$ (see the [OPTIONAL SLIDE](#EM-Algorithm) for more info) that essentially reduces FE minimization to maximum likelihood estimation. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualization of Constrained Free Energy Minimization\n", "\n", "- The following image by [David Blei](https://www.cs.columbia.edu/~blei/) illustrates the Variational Bayes approach:\n", "

\n", "- \n", "\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- The Bayesian posterior $p(z|x)$ (upper-right) is the posterior that would be obtained through executing Bayes rule, but unfortunately Bayes rule is not tractable here. Instead, we propose a variational posterior $q(z;\\nu)$ that is parameterized by $\\nu$. The inside area of the ellipsis represents the area that is reachable by choosing values for the parameter $\\nu$. Note that $p(z|x)$ is not reachable. We start the FE minimization process by choosing an initial value $\\nu^{\\text{init}}$, which corresponds to posterior $q(z;\\nu^{\\text{init}})$, as indicated in the figure. FE minimization leads to a final value $\\nu^{*}$ that minimizes the KL-divergence between $q(z;\\nu)$ and $p(z|x)$. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Challenge Revisited: Density Modeling for the Old Faithful Data Set\n", "\n", "- Let's get back to the illustrative challenge at the beginning of this lesson: we want to do [density modeling for the Old Faithful data set](#illustrative-example).\n", "\n", "#### model specification\n", "\n", "\n", "- We consider a Gaussian Mixture Model, specified by \n", "$$\\begin{align*}\n", "p(x,z|\\theta) &= p(x|z,\\mu,\\Lambda)p(z|\\pi) \\\\\n", "&= \\prod_{n=1}^N \\prod_{k=1}^K \\mathcal{N}\\left( x_n | \\mu_k, \\Lambda_k^{-1}\\right)^{z_{nk}} \\cdot \\prod_{n=1}^N \\prod_{k=1}^K \\pi_k^{z_{nk}} \\\\\n", " &= \\prod_{n=1}^N \\prod_{k=1}^K \\left(\\pi_k \\cdot \\mathcal{N}\\left( x_n | \\mu_k, \\Lambda_k^{-1}\\right)\\right)^{z_{nk}} \\tag{B-10.37,38}\n", "\\end{align*}$$\n", "\n", "- Let us introduce some priors for the parameters $\\pi$, $\\mu$ and $\\Lambda$. We factorize the prior and choose conjugate distributions by\n", "$$\n", "p(\\pi,\\mu,\\Lambda) = p(\\pi) p(\\mu|\\Lambda) p(\\Lambda)\n", "$$\n", "with \n", "$$\\begin{align*}\n", "p(\\pi) &= \\mathrm{Dir}(\\pi|\\alpha_0) = C(\\alpha_0) \\prod_k \\pi_k^{\\alpha_0-1} \\qquad &&\\text{(B-10.39)}\\\\\n", "p(\\mu|\\Lambda) &= \\prod_k \\mathcal{N}\\left(\\mu_k | m_0, \\left( \\beta_0 \\Lambda_k\\right)^{-1} \\right) \\qquad &&\\text{(B-10.40)}\\\\\n", "p(\\Lambda) &= \\prod_k \\mathcal{W}\\left( \\Lambda_k | W_0, \\nu_0 \\right) \\qquad &&\\text{(B-10.40)}\n", "\\end{align*}$$\n", "where $\\mathcal{W}\\left( \\cdot \\right)$ is a [Wishart distribution](https://en.wikipedia.org/wiki/Wishart_distribution) (i.e., a multi-dimensional Gamma distribution).\n", "- The full generative model is now specified by\n", "$$\n", "p(x,z,\\pi,\\mu,\\Lambda) = p(x|z,\\mu,\\Lambda) p(z|\\pi) p(\\pi) p(\\mu|\\Lambda) p(\\Lambda) \\tag{B-10.41}\n", "$$\n", "with hyperparameters $\\{ \\alpha_0, m_0, \\beta_0, W_0, \\nu_0\\}$.\n", "\n", "#### inference\n", "\n", "- Assume that we have observed $D = \\left\\{x_1, x_2, \\ldots, x_N\\right\\}$ and are interested to infer a posterior distribution for the parameters $\\pi$, $\\mu$ and $\\Lambda$. \n", "\n", "- We will approximate Bayesian inference by FE minimization. For the specified model, this leads to FE minimization w.r.t. the hyperparameters, i.e., we need to minimize the function \n", "$$F(\\alpha_0, m_0, \\beta_0, W_0, \\nu_0) \\,.$$\n", "\n", "- In general, this function can be optimized in various ways, e.g. by a gradient-descent procedure. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "- It turns out that adding the following **factorization constraints** on the posterior makes the FEM task analytically tractible:\n", "$$\\begin{equation}\n", "q(z,\\pi,\\mu,\\Lambda) = q(z) \\cdot q(\\pi,\\mu,\\Lambda) \\,. \\tag{B-10.42}\n", "\\end{equation}$$ \n", "\n", "- For this specific case (GMM model with assumed factorization and parameterization constraints), Bishop shows that the equations for the [optimal solutions (Eq. B-10.9)](#optimal-solutions) are analytically solvable, leading to the following variational update equations (for $k=1,\\ldots,K$): \n", "$$\n", "\\begin{align*}\n", "\\alpha_k &= \\alpha_0 + N_k \\tag{B-10.58} \\\\\n", "\\beta_k &= \\beta_0 + N_k \\tag{B-10.60} \\\\\n", "m_k &= \\frac{1}{\\beta_k} \\left( \\beta_0 m_0 + N_k \\bar{x}_k \\right) \\tag{B-10.61} \\\\\n", "W_k^{-1} &= W_0^{-1} + N_k S_k + \\frac{\\beta_0 N_k}{\\beta_0 + N_k}\\left( \\bar{x}_k - m_0\\right) \\left( \\bar{x}_k - m_0\\right)^T \\tag{B-10.62} \\\\\n", "\\nu_k &= \\nu_0 + N_k \\tag{B-10.63}\n", "\\end{align*}\n", "$$\n", "where we used\n", "$$\n", "\\begin{align*}\n", "\\log \\rho_{nk} &= \\mathbb{E}\\left[ \\log \\pi_k\\right] + \\frac{1}{2}\\mathbb{E}\\left[ \\log | \\Lambda_k | \\right] - \\frac{D}{2} \\log(2\\pi) \\\\ \n", " & \\qquad - \\frac{1}{2}\\mathbb{E}\\left[(x_k - \\mu_k)^T \\Lambda_k(x_k - \\mu_k) \\right] \\tag{B-10.46} \\\\\n", "r_{nk} &= \\frac{\\rho_{nk}}{\\sum_{j=1}^K \\rho_{nj}} \\tag{B-10.49} \\\\\n", "N_k &= \\sum_{n=1}^N r_{nk} x_n \\tag{B-10.51} \\\\\n", "\\bar{x}_k &= \\frac{1}{N_k} \\sum_{n=1}^N r_{nk} x_n \\tag{B-10.52} \\\\\n", "S_k &= \\frac{1}{N_k} \\sum_{n=1}^N r_{nk} \\left( x_n - \\bar{x}_k\\right) \\left( x_n - \\bar{x}_k\\right)^T \\tag{B-10.53}\n", "\\end{align*}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Exam guide: Working out FE minimization for the GMM to these update equations (eqs B-10.58 through B-10.63) is not something that you need to reproduce without assistance at the exam. Rather, the essence is that *it is possible* to arrive at closed-form variational update equations for the GMM. You should understand though how FEM works conceptually and in principle be able to derive variational update equations for very simple models that do not involve clever mathematical tricks." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code Example: FEM for GMM on Old Faithfull data set\n", "\n", "- Below we exemplify training of a Gaussian Mixture Model on the Old Faithful data set by Free Energy Minimization, using the constraints as specified above. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "using Pkg; Pkg.activate(\"../.\"); Pkg.instantiate();\n", "using IJulia; try IJulia.clear_output(); catch _ end" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "image/svg+xml": [ "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ], "text/html": [ "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "using DataFrames, CSV, LinearAlgebra, PDMats, SpecialFunctions, Random\n", "\n", "include(\"scripts/gmm_plot.jl\") # Holds plotting function \n", "old_faithful = CSV.read(\"datasets/old_faithful.csv\",DataFrame);\n", "X = convert(Matrix{Float64}, [old_faithful[!,1] old_faithful[!,2]]');#data matrix\n", "N = size(X, 2) #number of observations\n", "K = 6\n", "\n", "function sufficientStatistics(X,r,k::Int) #function to compute sufficient statistics\n", " N_k = sum(r[k,:])\n", " hat_x_k = sum([r[k,n]*X[:,n] for n in 1:N]) ./ N_k\n", " S_k = sum([r[k,n]*(X[:,n]-hat_x_k)*(X[:,n]-hat_x_k)' for n in 1:N]) ./ N_k\n", " return N_k, hat_x_k, S_k\n", "end\n", "\n", "function updateMeanPrecisionPi(m_0,β_0,W_0,ν_0,α_0,r) #variational maximisation function\n", " m = Array{Float64}(undef,2,K) #mean of the clusters \n", " β = Array{Float64}(undef,K) #precision scaling for Gausian distribution\n", " W = Array{Float64}(undef,2,2,K) #precision prior for Wishart distributions\n", " ν = Array{Float64}(undef,K) #degrees of freedom parameter for Wishart distribution\n", " α = Array{Float64}(undef,K) #Dirichlet distribution parameter \n", " for k=1:K\n", " sst = sufficientStatistics(X,r,k)\n", " α[k] = α_0[k] + sst[1]; β[k] = β_0[k] + sst[1]; ν[k] = ν_0[k] .+ sst[1]\n", " m[:,k] = (1/β[k])*(β_0[k].*m_0[:,k] + sst[1].*sst[2])\n", " W[:,:,k] = inv(inv(W_0[:,:,k])+sst[3]*sst[1] + ((β_0[k]*sst[1])/(β_0[k]+sst[1])).*(sst[2]-m_0[:,k])*(sst[2]-m_0[:,k])')\n", " end\n", " return m,β,W,ν,α\n", "end\n", "\n", "function updateR(Λ,m,α,ν,β) #variational expectation function\n", " r = Array{Float64}(undef,K,N) #responsibilities \n", " hat_π = Array{Float64}(undef,K) \n", " hat_Λ = Array{Float64}(undef,K)\n", " for k=1:K\n", " hat_Λ[k] = 1/2*(2*log(2)+logdet(Λ[:,:,k])+digamma(ν[k]/2)+digamma((ν[k]-1)/2))\n", " hat_π[k] = exp(digamma(α[k])-digamma(sum(α)))\n", " for n=1:N\n", " r[k,n] = hat_π[k]*exp(-hat_Λ[k]-1/β[k] - (ν[k]/2)*(X[:,n]-m[:,k])'*Λ[:,:,k]*(X[:,n]-m[:,k]))\n", " end\n", " end\n", " for n=1:N\n", " r[:,n] = r[:,n]./ sum(r[:,n]) #normalize to ensure r represents probabilities \n", " end\n", " return r\n", "end\n", "\n", "max_iter = 120\n", "#store the inference results in these vectors\n", "ν = fill!(Array{Float64}(undef,K,max_iter),3)\n", "β = fill!(Array{Float64}(undef,K,max_iter),1.0)\n", "α = fill!(Array{Float64}(undef,K,max_iter),0.01)\n", "R = Array{Float64}(undef,K,N,max_iter)\n", "M = Array{Float64}(undef,2,K,max_iter)\n", "Λ = Array{Float64}(undef,2,2,K,max_iter)\n", "clusters_vb = Array{Distribution}(undef,K,max_iter) #clusters to be plotted\n", "#initialize prior distribution parameters\n", "M[:,:,1] = rand(MersenneTwister(42), 2, K) .* [4, 50] .+ [1, 50]\n", "for k=1:K\n", " Λ[:,:,k,1] = [1.0 0;0 0.01]\n", " R[k,:,1] = 1/(K)*ones(N)\n", " clusters_vb[k,1] = MvNormal(M[:,k,1],PDMats.PDMat(convert(Matrix,Hermitian(inv(ν[1,1].*Λ[:,:,k,1])))))\n", "end\n", "#variational inference\n", "for i=1:max_iter-1\n", " #variational expectation \n", " R[:,:,i+1] = updateR(Λ[:,:,:,i],M[:,:,i],α[:,i],ν[:,i],β[:,i]) \n", " #variational minimisation\n", " M[:,:,i+1],β[:,i+1],Λ[:,:,:,i+1],ν[:,i+1],α[:,i+1] = updateMeanPrecisionPi(M[:,:,i],β[:,i],Λ[:,:,:,i],ν[:,i],α[:,i],R[:,:,i+1])\n", " for k=1:K\n", " clusters_vb[k,i+1] = MvNormal(M[:,k,i+1],PDMats.PDMat(convert(Matrix,Hermitian(inv(ν[k,i+1].*Λ[:,:,k,i+1])))))\n", " end\n", "end\n", "\n", "include(\"scripts/gmm_plot.jl\") # Holds plotting function \n", "plots = [plotGMM(X, clusters_vb[:,1], R[:,:,1], \"Initial situation\")]\n", "for i=LinRange(2, 120, 5)\n", " i = round(Int,i)\n", " push!(plots, plotGMM(X, clusters_vb[:,i], R[:,:,i], \"After $(i) iterations\"))\n", "end\n", "plot(plots..., layout=(2,3), size=(1100, 600))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- The generated figure looks much like Figure 10.6 in Bishop. The plots show FEM results for a GMM of $K = 6$ Gaussians applied to the Old Faithful data set. The ellipses denote the one standard-deviation density contours for each of the components, and the color coding of the data points reflects the \"soft\" class label assignments. Components whose expected mixing coefficient are numerically indistinguishable from zero are not plotted." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Variational Inference and The Method of Maximum Entropy\n", "\n", "- We derived variational inference by substituting a variational posterior $q(z)$ for the Bayesian posterior $p(z|x)$ in the CA decomposition of (negative log) Bayesian evidence for a model. This is clever, but reveals nothing about the foundations of variational inference. Is variational inference any good?\n", "\n", "- In [Caticha (2010)](https://arxiv.org/abs/1011.0723) (based on earlier work by [Shore and Johnson (1980)](https://github.com/bertdv/BMLIP/blob/master/lessons/notebooks/files/ShoreJohnson-1980-Axiomatic-Derivation-of-the-Principle-of-Maximum-Entropy.pdf)), the **Method of Maximum (Relative) Entropy** is developed for rational updating of priors to posteriors when faced with new information in the form of constraints. Caticha's argumentation is as follows:\n", "\n", " - Consider prior beliefs (ie, a generative model) $p(x,z)$ about observed and latent variables $x$ and $z$. Assume that new information in the form of (data, factorization or form) constraints is obtained and we are interested in the \"best update\" to a posterior $q(x,z)$.\n", "\n", " - We first establish that new observations of $x$ can be phrased as constraints on the variational posterior $q$. For instance, a new observation $x_1=5$ can be formulated as a posterior constraint $q(x_1)=\\delta(x_1-5)$. \n", "\n", " - In order to define what \"best update\" means, Caticha assumed a ranking function $S[q]$ that generates a preference score for each candidate posterior $q$ for a given prior $p$. The best update from $p$ to $q$ is then identified as $$q^* = \\arg\\max_q S[q]\\,, \\quad \\text{subject to constraints.} $$ \n", "\n", " - Similarly to [Cox' method](https://en.wikipedia.org/wiki/Cox%27s_theorem) for deriving Probability Theory from a set of sensical assumptions, Caticha then introduced the following axioms, based on a rational principle (the **principle of minimal updating**, see [Caticha 2010](https://arxiv.org/abs/1011.0723)), that the ranking function needs to adhere to: \n", " 1. *Locality*: local information has local effects.\n", " 2. *Coordinate invariance*: the system of coordinates carries no information. \n", " 3. *Independence*: When systems are known to be independent, it should not matter whether they are treated separately or jointly. \n", " \n", " - It turns out that these three criteria **uniquely identify the Relative Entropy** as the proper ranking function: \n", "$$\\begin{align*}\n", "S[q] = - \\sum_z q(x,z) \\log \\frac{q(x,z)}{p(x,z)}\n", "\\end{align*}$$\n", "\n", " - This procedure to find the variational posterior $q$ is called the Method of Maximum (Relative) Entropy (MRE). Note that, since $S[q]=-F[q]$, constrained Relative Entropy maximization is equivalent to constrained Free Energy minimization! \n", "\n", "- $\\Rightarrow$ When information is supplied in the form of constraints on the posterior (such as form/factorization constraints and new observations as data constraints), we *should* select the posterior that minimizes the constrained Free Energy. **Constrained FE minimization is the proper method for inference!**\n", "\n", "- Bayes rule is the global solution of constrained FEM when all constraints are data constraints, ie, delta distributions on $q(x)$. Hence, Bayes rule is a special case of constrained FEM. Bayes rule only applies to updating belief on the basis of new observations. FE minimization is the best inference method you can do under the given constraints. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Interesting Decompositions of the Free Energy Functional\n", "\n", "\n", "- In rounding up this lession, we summarize a few interesting decompositions of the FE functional, making use of $p(x,z) = p(z|x)p(x) = p(x|z)p(z)$ \n", "\n", "$$\\begin{align*}\n", "\\mathrm{F}[q] &\\triangleq \\sum_z q(z) \\log \\frac{q(z)}{p(x,z)} \\\\\n", "&= \\underbrace{\\sum_z q(z) \\log \\frac{1}{p(x,z)}}_{\\text{energy}} - \\underbrace{\\sum_z q(z) \\log \\frac{1}{q(z)}}_{\\text{entropy}} \\qquad &&\\text{(EE)} \\\\\n", "&= \\underbrace{\\sum_z q(z) \\log \\frac{q(z)}{p(z|x)}}_{\\text{inference bound}\\geq 0} - \\underbrace{\\log p(x)}_{\\text{log-evidence}} \\qquad &&\\text{(BE)} \\\\\n", "&= \\underbrace{\\sum_z q(z)\\log\\frac{q(z)}{p(z)}}_{\\text{complexity}} - \\underbrace{\\sum_z q(z) \\log p(x|z)}_{\\text{accuracy}} \\qquad &&\\text{(CA)}\n", "\\end{align*}$$\n", "\n", "- These decompositions are very insightful and we will label them respectively as _energy-entropy_ (EE), _bound-evidence_ (BE), and _complexity-accuracy_ (CA) decompositions. \n", "\n", "- In the [Bayesian Machine Learning](https://nbviewer.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Bayesian-Machine-Learning.ipynb) lecture, we discussed the CA decomposition of Bayesian model evidence to support the interpretation of evidence as a model performance criterion. Here, we recognize that FE allows a similar CA decomposition: minimizing FE increases data fit and decreases model complexity. Hence, FE is a good model performance criterion.\n", "\n", "- The CA decomposition makes use of the prior $p(z)$ and likelihood $p(x|z)$, both of which are selected by the engineer, so the FE can be evaluated with this decomposition!\n", "\n", "- The BE decomposition restates what we derived earlier, namely that the FE is an upperbound on the (negative) log-evidence. The bound is the KL-divergence between the variational posterior $q(z)$ and the (perfect) Bayesian posterior $p(z|x)$. Global minimization of FE with only data constraints drives the KL-divergence to zero and results to perfect Bayesian inference.\n", "\n", "- The BE decomposition can also be interpreted as problem representation costs (negative log-evidence) plus solution proposal costs (the KL-divergence bound), see the [Intelligent Agent and Active Inference lesson (slide on Problem and Solution costs)](https://nbviewer.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Intelligent-Agents-and-Active-Inference.ipynb#PS-decomposition) for more details.\n", "\n", "- The EE decomposition provides a link to the [second law of thermodynamics](https://en.wikipedia.org/wiki/Second_law_of_thermodynamics): Minimizing FE leads to entropy maximization, subject to constraints, where in this case the constraints are imposed by the postulated generative model. \n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Variational Inference in Practice\n", "\n", "- For most interesting models of real-world problems, Bayes rule is not tractible. Therefore, the usage of approximate variational Bayesian inference in real-world settings is rising rapidly.\n", "\n", "- A toolbox such as [RxInfer](http://rxinfer.ml) makes it possible to specify a complex model and automate the inference process by constrained Free Energy minimization. \n", "\n", "- Note that model specification, even for complex models, usually does not take more than 1 page of code. As a result, you can, in principle, solve very complex problems by automated inference in a complex model with less than 1 page of code. \n", "\n", "- $\\Rightarrow$ Compared to writing an application algorithm of, say 40 pages of code, solving problems by automated variational inference is potentially a big deal for the future design of information processing systems. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##
OPTIONAL SLIDES
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FE Minimization with Mean-field Factorization Constraints: the CAVI Approach \n", "\n", "- Let's work out FE minimization with additional mean-field constraints (=full factorization) constraints: $$q(z) = \\prod_{j=1}^m q_j(z_j)\\,.$$\n", "\n", " - In other words, the posteriors for $z_j$ are all considered independent. This is a strong constraint but leads often to good solutions.\n", " \n", "- Given the mean-field constraints, it is possible to derive the following expression for the optimal solutions $q_j^*(z_j)$, for $j=1,\\ldots,m$: \n", "\n", "\\begin{equation*} \\tag{B-10.9}\n", "\\boxed{\n", "\\begin{aligned}\n", "\\log q_j^*(z_j) &\\propto \\mathrm{E}_{q_{-j}^*}\\left[ \\log p(x,z) \\right] \\\\\n", " &= \\underbrace{\\sum_{z_{-j}} q_{-j}^*(z_{-j}) \\underbrace{\\log p(x,z)}_{\\text{\"field\"}}}_{\\text{\"mean field\"}} \n", "\\end{aligned}}\n", "\\end{equation*}\n", "\n", "where we defined $q_{-j}^*(z_{-j}) \\triangleq q_1^*(z_1)q_2^*(z_2)\\cdots q_{j-1}^*(z_{j-1})q_{j+1}^*(z_{j+1})\\cdots q_m^*(z_m)$.\n", "- **Proof** (from [Blei, 2017](https://www.tandfonline.com/doi/full/10.1080/01621459.2017.1285773)): We first rewrite the FE as a function of $q_j(z_j)$ only: \n", " $$ F[q_j] = \\mathbb{E}_{q_{j}}\\left[ \\mathbb{E}_{q_{-j}}\\left[ \\log p(x,z_j,z_{-j})\\right]\\right] - \\mathbb{E}_{q_j}\\left[ \\log q_j(z_j)\\right] + \\mathtt{const.}\\,,$$\n", " where the constant holds all terms that do not depend on $z_j$. This expression can be written as \n", " $$ F[q_j] = \\sum_{z_j} q_j(z_j) \\log \\frac{q_j(z_j)}{\\exp\\left( \\mathbb{E}_{q_{-j}}\\left[ \\log p(x,z_j,z_{-j})\\right]\\right)}$$\n", " which is a KL-divergence that is minimized by Eq. B-10.9. (end proof)\n", " \n", "- This is not yet a full solution to the FE minimization task since the solution $q_j^*(z_j)$ depends on expectations that involve other solutions $q_{i\\neq j}^*(z_{i \\neq j})$, and each of these other solutions $q_{i\\neq j}^*(z_{i \\neq j})$ depends on an expection that involves $q_j^*(z_j)$. \n", "- In practice, we solve this chicken-and-egg problem by an iterative approach: we first initialize all $q_j(z_j)$ (for $j=1,\\ldots,m$) to an appropriate initial distribution and then cycle through the factors in turn by solving eq.B-10.9 and update $q_{-j}^*(z_{-j})$ with the latest estimates. (See [Blei, 2017](https://www.tandfonline.com/doi/full/10.1080/01621459.2017.1285773), Algorithm 1, p864). \n", "\n", "- This algorithm for approximating Bayesian inference is known **Coordinate Ascent Variational Inference** (CAVI). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FE Minimization by the Expectation-Maximization (EM) Algorithm\n", "\n", "- The EM algorithm is a special case of FE minimization that focusses on Maximum-Likelihood estimation for models with latent variables. \n", "- Consider a model $$p(x,z,\\theta)$$ with observations $x = \\{x_n\\}$, latent variables $z=\\{z_n\\}$ and parameters $\\theta$.\n", "\n", "- We can write the following FE functional for this model:\n", "$$\\begin{align*}\n", "F[q] = \\sum_z \\sum_\\theta q(z,\\theta) \\log \\frac{q(z,\\theta)}{p(x,z,\\theta)} \n", "\\end{align*}$$\n", "\n", "- The EM algorithm makes the following simplifying assumptions:\n", " 1. The prior for the parameters is uninformative (uniform). This implies that \n", " $$p(x,z,\\theta) = p(x,z|\\theta) p(\\theta) \\propto p(x,z|\\theta)$$\n", " 2. A factorization constraint $$q(z,\\theta) = q(z) q(\\theta)$$\n", " 3. The posterior for the parameters is a delta function:\n", " $$q(\\theta) = \\delta(\\theta - \\hat{\\theta})$$\n", " \n", "\n", " \n", "- Basically, these three assumptions turn FE minimization into maximum likelihood estimation for the parameters $\\theta$ and the FE simplifies to \n", "$$\\begin{align*}\n", "F[q,\\theta] = \\sum_z q(z) \\log \\frac{q(z)}{p(x,z|\\theta)} \n", "\\end{align*}$$\n", "\n", "- The EM algorithm minimizes this FE by iterating (iteration counter: $i$) over \n", "\n", "\\begin{equation*}\n", "\\boxed{\n", "\\begin{aligned}\n", "\\mathcal{L}^{(i)}(\\theta) &= \\sum_z \\overbrace{p(z|x,\\theta^{(i-1)})}^{q^{(i)}(z)} \\log p(x,z|\\theta) \\quad &&\\text{the E-step} \\\\\n", "\\theta^{(i)} &= \\arg\\max_\\theta \\mathcal{L}^{(i)}(\\theta) &&\\text{the M-step}\n", "\\end{aligned}}\n", "\\end{equation*}\n", "\n", "- These choices are optimal for the given FE functional. In order to see this, consider the two decompositions\n", "$$\\begin{align*}\n", "F[q,\\theta] &= \\underbrace{-\\sum_z q(z) \\log p(x,z|\\theta)}_{\\text{energy}} - \\underbrace{\\sum_z q(z) \\log \\frac{1}{q(z)}}_{\\text{entropy}} \\qquad &&\\text{(EE)}\\\\\n", " &= \\underbrace{\\sum_z q(z) \\log \\frac{q(z)}{p(z|x,\\theta)}}_{\\text{divergence}} - \\underbrace{\\log p(x|\\theta)}_{\\text{log-likelihood}} \\qquad &&\\text{(DE)}\n", "\\end{align*}$$\n", "\n", "- The DE decomposition shows that the FE is minimized for the choice $q(z) := p(z|x,\\theta)$. Also, for this choice, the FE equals the (negative) log-evidence (, which is this case simplifies to the log-likelihood). \n", "\n", "- The EE decomposition shows that the FE is minimized wrt $\\theta$ by minimizing the energy term. The energy term is computed in the E-step and optimized in the M-step.\n", " - Note that in the EM literature, the energy term is often called the _expected complete-data log-likelihood_.)\n", "\n", "- In order to execute the EM algorithm, it is assumed that we can analytically execute the E- and M-steps. For a large set of models (including models whose distributions belong to the exponential family of distributions), this is indeed the case and hence the large popularity of the EM algorithm. \n", "\n", "- The EM algorihm imposes rather severe assumptions on the FE (basically approximating Bayesian inference by maximum likelihood estimation). Over the past few years, the rise of Probabilistic Programming languages has dramatically increased the range of models for which the parameters can by estimated autmatically by (approximate) Bayesian inference, so the popularity of EM is slowly waning. (More on this in the Probabilistic Programming lessons). \n", "\n", "- Bishop (2006) works out EM for the GMM in section 9.2." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code Example: EM-algorithm for the GMM on the Old-Faithful data set\n", "\n", "We'll perform clustering on the data set from the [illustrative example](#illustrative-example) by fitting a GMM consisting of two Gaussians using the EM algorithm. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "image/svg+xml": [ "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ], "text/html": [ "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "using DataFrames, CSV, LinearAlgebra\n", "include(\"scripts/gmm_plot.jl\") # Holds plotting function \n", "old_faithful = CSV.read(\"datasets/old_faithful.csv\", DataFrame);\n", "\n", "X = Array(Matrix{Float64}(old_faithful)')\n", "N = size(X, 2)\n", "\n", "# Initialize the GMM. We assume 2 clusters.\n", "clusters = [MvNormal([4.;60.], [.5 0;0 10^2]); \n", " MvNormal([2.;80.], [.5 0;0 10^2])];\n", "π_hat = [0.5; 0.5] # Mixing weights\n", "γ = fill!(Matrix{Float64}(undef,2,N), NaN) # Responsibilities (row per cluster)\n", "\n", "# Define functions for updating the parameters and responsibilities\n", "function updateResponsibilities!(X, clusters, π_hat, γ)\n", " # Expectation step: update γ\n", " norm = [pdf(clusters[1], X) pdf(clusters[2], X)] * π_hat\n", " γ[1,:] = (π_hat[1] * pdf(clusters[1],X) ./ norm)'\n", " γ[2,:] = 1 .- γ[1,:]\n", "end\n", "function updateParameters!(X, clusters, π_hat, γ)\n", " # Maximization step: update π_hat and clusters using ML estimation\n", " m = sum(γ, dims=2)\n", " π_hat = m / N\n", " μ_hat = (X * γ') ./ m'\n", " for k=1:2\n", " Z = (X .- μ_hat[:,k])\n", " Σ_k = Symmetric(((Z .* (γ[k,:])') * Z') / m[k])\n", " clusters[k] = MvNormal(μ_hat[:,k], convert(Matrix, Σ_k))\n", " end\n", "end\n", "\n", "# Execute the algorithm: iteratively update parameters and responsibilities\n", "plots = [plotGMM(X, clusters, γ, \"Initial situation\")]\n", "updateResponsibilities!(X, clusters, π_hat, γ)\n", "push!(plots, plotGMM(X, clusters, γ, \"After first E-step\"))\n", "updateParameters!(X, clusters, π_hat, γ)\n", "push!(plots, plotGMM(X, clusters, γ, \"After first M-step\"))\n", "iter_counter = 1\n", "for i=1:3\n", " for j=1:i+1\n", " updateResponsibilities!(X, clusters, π_hat, γ)\n", " updateParameters!(X, clusters, π_hat, γ)\n", " iter_counter += 1\n", " end\n", " push!(plots, plotGMM(X, clusters, γ, \"After $(iter_counter) iterations\"))\n", "end\n", "plot(plots..., layout=(2,3), size=(1100, 600))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Message Passing for Free Energy Minimization\n", "\n", "- The Sum-Product (SP) update rule implements perfect Bayesian inference. \n", "- Sometimes, the SP update rule is not analytically solvable. \n", "- Fortunately, for many well-known Bayesian approximation methods, a message passing update rule can be created, e.g. [Variational Message Passing](https://en.wikipedia.org/wiki/Variational_message_passing) (VMP) for variational inference. \n", "- In general, all of these message passing algorithms can be interpreted as minimization of a constrained free energy (e.g., see [Senoz et al. (2021)](https://www.mdpi.com/1099-4300/23/7/807), and hence these message passing schemes comply with [Caticha's Method of Maximum Relative Entropy](https://arxiv.org/abs/1011.0723), which, as discussed in the [variational Bayes lesson](https://nbviewer.jupyter.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Latent-Variable-Models-and-VB.ipynb) is the proper way for updating beliefs. \n", "- Different message passing updates rules can be combined to get a hybrid inference method in one model. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The Local Free Energy in a Factor Graph \n", "\n", "- Consider an edge $x_j$ in a Forney-style factor graph for a generative model $p(x) = p(x_1,x_2,\\ldots,x_N)$.\n", "\n", "\n", "\n", "- Assume that the graph structure (factorization) is specified by\n", "$$\n", "p(x) = \\prod_{a=1}^M p_a(x_a)\n", "$$\n", "where $a$ is a set of indices.\n", "- Also, we assume a mean-field approximation for the posterior:\n", "$$\n", "q(x) = \\prod_{i=1}^N q_i(x_i)\n", "$$\n", "and consequently a corresponding free energy functional \n", "$$\\begin{align*}\n", "F[q] &= \\sum_x q(x) \\log \\frac{q(x)}{p(x)} \\\\\n", " &= \\sum_i \\sum_{x_i} \\left(\\prod_{i=1}^N q_i(x_i)\\right) \\log \\frac{\\prod_{i=1}^N q_i(x_i)}{\\prod_{a=1}^M p_a(x_a)}\n", "\\end{align*}$$\n", "\n", "- With these assumptions, it can be shown that the FE evaluates to (exercise)\n", "$$\n", "F[q] = \\sum_{a=1}^M \\underbrace{\\sum_{x_a} \\left( \\prod_{j\\in N(a)} q_j(x_j)\\cdot \\left(-\\log p_a(x_a)\\right) \\right) }_{\\text{node energy }U[p_a]} - \\sum_{i=1}^N \\underbrace{\\sum_{x_i} q_i(x_i) \\log \\frac{1}{q_i(x_i)}}_{\\text{edge entropy }H[q_i]}\n", "$$\n", "\n", "- In words, the FE decomposes into a sum of (expected) energies for the nodes minus the entropies on the edges. \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Variational Message Passing\n", "\n", "- Let us now consider the local free energy that is associated with edge corresponding to $x_j$. \n", " \n", "

\n", " \n", " \n", "- Apparently (see previous slide), there are three contributions to the free energy for $x_j$:\n", " - one entropy term for the edge $x_j$\n", " - two energy terms: one for each node that attaches to $x_j$ (in the figure: nodes $p_a$ and $p_b$)\n", " \n", "- The local free energy for $x_j$ can be written as (exercise)\n", " $$\n", " F[q_j] \\propto \\sum_{x_j} q(x_j) \\log \\frac{q_j(x_j)}{\\nu_a(x_j)\\cdot \\nu_b(x_j)}\n", " $$\n", " where\n", " $$\\begin{align*} \n", " \\nu_a(x_j) &\\propto \\exp\\left( \\mathbb{E}_{q_{k}}\\left[ \\log p_a(x_a)\\right]\\right) \\\\\n", " \\nu_b(x_j) &\\propto \\exp\\left( \\mathbb{E}_{q_{l}}\\left[ \\log p_b(x_b)\\right]\\right) \n", " \\end{align*}$$\n", " and $\\mathbb{E}_{q_{k}}\\left[\\cdot\\right]$ is an expectation w.r.t. all $q(x_k)$ with $k \\in N(a)\\setminus {j}$.\n", " \n", "- $\\nu_a(x_j)$ and $\\nu_b(x_j)$ can be locally computed in nodes $a$ and $b$ respectively and can be interpreted as colliding messages over edge $x_j$. \n", " \n", "- Local free energy minimization is achieved by setting\n", " $$\n", " q_j(x_j) \\propto \\nu_a(x_j) \\cdot \\nu_b(x_j)\n", " $$\n", " \n", "- Note that message $\\nu_a(x_j)$ depends on posterior beliefs over incoming edges ($k$) for node $a$, and in turn, the message from node $a$ towards edge $x_k$ depends on the belief $q_j(x_j)$. I.o.w., direct mutual dependencies exist between posterior beliefs over edges that attach to the same node. \n", " \n", "- These considerations lead to the [Variational Message Passing](https://en.wikipedia.org/wiki/Variational_message_passing) procedure, which is an iterative free energy minimization procedure that can be executed completely through locally computable messages. \n", "\n", "- Procedure VMP, see [Dauwels (2007), section 3](https://github.com/bertdv/BMLIP/blob/master/lessons/notebooks/files/Dauwels-2007-on-variational-message-passing-on-factor-graphs.pdf)\n", " > 1. Initialize all messages $q$ and $ν$, e.g., $q(\\cdot) \\propto 1$ and $\\nu(\\cdot) \\propto 1$.
\n", " > 2. Select an edge $z_k$ in the factor graph of $f(z_1,\\ldots,z_m)$.
\n", " > 3. Compute the two messages $\\overrightarrow{\\nu}(z_k)$ and $\\overleftarrow{\\nu}(z_k)$ by applying the following generic rule:\n", " $$\n", " \\overrightarrow{\\nu}(y) \\propto \\exp\\left( \\mathbb{E}_{q}\\left[ \\log g(x_1,\\dots,x_n,y)\\right] \\right) \n", " $$\n", " > 4. Compute the marginal $q(z_k)$\n", " $$\n", " q(z_k) \\propto \\overrightarrow{\\nu}(z_k) \\overleftarrow{\\nu}(z_k)\n", " $$\n", " and send it to the two nodes connected to the edge $x_k$.
\n", " > 5. Iterate 2–4 until convergence. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### The Bethe Free Energy and Belief Propagation\n", "\n", "- We showed that, under mean field assumptions, the FE can be decomposed into a sum of local FE contributions for the nodes ($a$) and edges ($i$):\n", "$$\\begin{align*}\n", "F[q] = \\sum_{a=1}^M \\underbrace{\\sum_{x_a} \\left( \\prod_{j\\in N(a)} q_j(x_j)\\cdot \\left(-\\log p_a(x_a)\\right) \\right) }_{\\text{node energy }U[p_a]} - \\sum_{i=1}^N \\underbrace{\\sum_{x_i} q_i(x_i) \\log \\frac{1}{q_i(x_i)}}_{\\text{edge entropy }H[q_i]}\n", "\\end{align*}$$\n", "\n", "- The mean field assumption is very strong and may lead to large inference costs ($\\mathrm{KL}(q(x),p(x|\\text{data}))$). A more relaxed assumption is to allow joint posterior beliefs over the variables that attach to a node. This idea is expressed by the Bethe Free Energy:\n", "$$\\begin{align*}\n", "F_B[q] = \\sum_{a=1}^M \\left( \\sum_{x_a} q_a(x_a) \\log \\frac{q_a(x_a)}{p_a(x_a)} \\right) - \\sum_{i=1}^N (d_i - 1) \\sum_{x_i} q_i(x_i) \\log {q_i(x_i)}\n", "\\end{align*}$$\n", "where $q_a(x_a)$ is the posterior joint belief over the variables $x_a$ (i.e., the set of variables that attach to node $a$), $q_i(x_i)$ is the posterior marginal belief over the variable $x_i$ and $d_i$ is the number of factor nodes that link to edge $i$. Moreover, $q_a(x_a)$ and $q_i(x_i)$ are constrained to obey the following equalities:\n", "$$\\begin{align*}\n", " \\sum_{x_a \\backslash x_i} q_a(x_a) &= q_i(x_i), ~~~ \\forall i, \\forall a \\\\\n", " \\sum_{x_i} q_i(x_i) &= 1, ~~~ \\forall i \\\\\n", " \\sum_{x_a} q_a(x_a) &= 1, ~~~ \\forall a \\\\\n", "\\end{align*}$$\n", "\n", "- We form the Lagrangian by augmenting the Bethe Free Energy functional with the constraints:\n", "$$\\begin{align*}\n", "L[q] = F_B[q] + \\sum_i\\sum_{a \\in N(i)} \\lambda_{ai}(x_i) \\left(q_i(x_i) - \\sum_{x_a\\backslash x_i} q(x_a) \\right) + \\sum_{i} \\gamma_i \\left( \\sum_{x_i}q_i(x_i) - 1\\right) + \\sum_{a}\\gamma_a \\left( \\sum_{x_a}q_a(x_a) -1\\right)\n", "\\end{align*}$$\n", "\n", "- The stationary solutions for this Lagrangian are given by\n", "$$\\begin{align*}\n", "q_a(x_a) &= f_a(x_a) \\exp\\left(\\gamma_a -1 + \\sum_{i \\in N(a)} \\lambda_{ai}(x_i)\\right) \\\\ \n", "q_i(x_i) &= \\exp\\left(1- \\gamma_i + \\sum_{a \\in N(i)} \\lambda_{ai}(x_i)\\right) ^{\\frac{1}{d_i - 1}}\n", "\\end{align*}$$\n", "where $N(i)$ denotes the factor nodes that have $x_i$ in their arguments and $N(a)$ denotes the set of variables in the argument of $f_a$.\n", "\n", "- Stationary solutions are functions of Lagrange multipliers. This means that Lagrange multipliers need to be determined. Lagrange multipliers can be determined by plugging the stationary solutions back into the constraint specification and solving for the multipliers which ensure that the constraint is satisfied. The first constraint we consider is normalization, which yields the following identification:\n", "$$\\begin{align*}\n", "\\gamma_a &= 1 - \\log \\Bigg(\\sum_{x_a}f_a(x_a)\\exp\\left(\\sum_{i \\in N(a)}\\lambda_{ai}(x_i)\\right)\\Bigg)\\\\\n", "\\gamma_i &= 1 + (d_i-1) \\log\\Bigg(\\sum_{x_i}\\exp\\left( \\frac{1}{d_i-1}\\sum_{a \\in N(i)} \\lambda_{ai}(x_i)\\right)\\Bigg).\n", "\\end{align*}$$\n", "\n", "- The functional form of the Lagrange multipliers that corresponds to the normalization constraint enforces us to obtain the Lagrange multipliers that correspond to the marginalization constraint. To do so we solve for \n", "$$\\begin{align*} \\sum_{x_a \\backslash x_i} f_a(x_a) \\exp\\left(\\sum_{i \\in N(a)} \\lambda_{ai}(x_i)\\right) &= \\exp\\left(\\sum_{a \\in N(i)} \\lambda_{ai}(x_i)\\right) ^{\\frac{1}{d_i - 1}} \\exp\\left(\\lambda_{ai}(x_i)\\right)\\sum_{x_a \\backslash x_i} f_a(x_a) \\exp\\Bigg(\\sum_{\\substack{{j \\in N(a)} j \\neq i}}\\lambda_{aj}(x_j)\\Bigg) \\\\\n", "&= \\exp\\left(\\sum_{a \\in N(i)} \\lambda_{ai}(x_i)\\right) ^{\\frac{1}{d_i - 1}} \\exp\\left(\\lambda_{ai}(x_i) + \\lambda_{ia}(x_i)\\right) \\\\\n", "&= \\exp\\left(\\sum_{a \\in N(i)} \\lambda_{ai}(x_i)\\right) ^{\\frac{1}{d_i - 1}}\\, , \n", "\\end{align*}$$ \n", "where we defined an auxilary function\n", "$$\\begin{align*}\n", "\\exp(\\lambda_{ia}(x_i)) \\triangleq \\sum_{x_a \\backslash x_i} f_a(x_a) \\exp\\Bigg(\\sum_{\\substack{{j \\in N(a)} j \\neq i}}\\lambda_{aj}(x_j)\\Bigg) \\,.\n", "\\end{align*}$$\n", "This definition is valid since it can be inverted by the relation\n", "$$\\begin{align*}\n", "\\lambda_{ia}(x_i) = \\frac{2-d_i}{d_i - 1}\\lambda_{ai}(x_i) + \\frac{1}{d_i -1}\\sum_{\\substack{c \\in N(i)\\\\c \\neq a}}\\lambda_{ci}(x_i)\n", "\\end{align*}$$\n", "\n", "- In general it is not possible to solve for the Lagrange multipliers analytically and we resort to iteratively obtaining the solutions. This leads to the **Belief Propagation algorithm** where the exponentiated Lagrange multipliers (messages) are updated iteratively via \n", "$$\\begin{align*} \n", "\\mu_{ia}^{(k+1)}(x_i) &= \\sum_{x_a \\backslash x_i} f_a(x_a) \\prod_{\\substack{{j \\in N(a)} j \\neq i}}\\mu^{(k)}_{aj}(x_j) \\mu_{ai}^{(k)}(x_i) \\\\\n", "&= \\prod_{\\substack{c \\in N(i) c \\neq a}}\\mu^{(k)}_{ic}(x_i)\\,, \n", "\\end{align*}$$ \n", "where $k$ denotes iteration number and the messages are defined as\n", "$$\\begin{align*}\n", "\\mu_{ia}(x_i) &\\triangleq \\exp(\\lambda_{ia}(x_i))\\\\\n", "\\mu_{ai}(x_i) &\\triangleq \\exp(\\lambda_{ai}(x_i))\\,.\n", "\\end{align*}$$\n", "\n", "- For a more complete overview of message passing as Bethe Free Energy minimization, see [Senoz et al. (2021)](https://www.mdpi.com/1099-4300/23/7/807)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "open(\"../../styles/aipstyle.html\") do f\n", " display(\"text/html\", read(f, String))\n", "end" ] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "anaconda-cloud": {}, "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Julia 1.10.2", "language": "julia", "name": "julia-1.10" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.10.5" } }, "nbformat": 4, "nbformat_minor": 4 }