{ "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)\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": [ "### Illustrative Example : 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):\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", "$$\\begin{align*}\n", "p(x_n,z_n) &= p(x_n | z_n) p(z_n) = \\prod_{k=1}^K (\\underbrace{\\pi_k \\cdot \\mathcal{N}\\left( x_n | \\mu_k, \\Sigma_k\\right) }_{p(x_n,z_{nk}=1)})^{z_{nk}} \n", "\\end{align*}$$\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 $\\pi_k$ 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 cluster ($k$) 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": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- By adding model structure through (equations among) latent variables, we can build more accurate models for very complex processes. Unfortunately, adding structure through 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 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 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", "- 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-10.2 an _Evidence Lower BOund_ (in modern machine learning literature abbreviated as **ELBO**) $\\mathcal{L}(z)$ that equals the _negative_ FE. 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", "- Generally, it is common to add 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", " 1. #### mean-field factorization\n", " - 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", " 2. #### form constraints\n", " - We constrain the posterior to be part of a 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_ $F(\\mu,\\Sigma)$ w.r.t. its parameters. We can often use standand gradient-based optimization methods to minimize the FE. \n", " - The following image by [David Blei](https://www.cs.columbia.edu/~blei/) illustrates this approach:\n", "

\n", "\n", " 3. #### the Expectation-Minimization (EM) algorithm\n", " - Here, we place 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. \n", " - As discussed before, maximum likelihood estimation is generally inferior to Bayesian inference, but it is often computationally attractive when a large data set is available.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Observations can also be rephrased as constraints on the posterior, e.g., a new observation $x_1=5$ can be phrased as a posterior constraint $q(x_1)=\\delta(x_1-5)$.\n", "\n", "- While Bayes rule only deals with computing a posterior when new data constraints (i.e., observations) have become available, inference by FEM can be executed any time when new information becomes available in the form of more general constraints, including both data constraints (new observations), and factorization and form constraints. \n", "\n", "- On that view, updating a prior to a posterior by constrained FEM is more general than Bayes rule. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Example: FEM for the Gaussian Mixture Model (CAVI Approach)\n", "\n", "- Let's get back to the illustrative example 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": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using Pkg; Pkg.activate(\"../.\"); Pkg.instantiate();\n", "using IJulia; try IJulia.clear_output(); catch _ end" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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"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] = [[3.0; 60.0];[4.0; 70.0];[2.0; 50.0];[2.0; 60.0];[3.0; 100.0];[4.0; 80.0]]\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=(1500, 900))\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:\n", "\n", " - Consider prior beliefs $p(z)$ about latent variables $z$. New information in the form of (data, factorization or form) constraints is obtained and we are interested in the \"best update\" to a posterior $q(z)$.\n", "\n", " - In order to define what \"best update\" means, Caticha assumed a ranking function $S_p[q]$ 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 $$q^* = \\arg\\max_q S_p[q]\\,, \\quad \\text{subject to constraints.} $$ \n", "\n", " - Similarly to [Cox' method](https://en.wikipedia.org/wiki/Cox%27s_theorem) for deriving Probability Theory from a set of sensical axioms, 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", " - These three criteria **uniquely identify the Relative Entropy** as the proper ranking function: \n", "$$\\begin{align*}\n", "S_p[q] = - \\sum_z q(z) \\log \\frac{q(z)}{p(z)}\n", "\\end{align*}$$\n", "\n", " - This procedure is called the Method of Maximum (Relative) Entropy (MRE). Note that 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 (including 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. Hence, Bayes rule is a special case of constrained FEM. " ] }, { "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 the various 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": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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uX78uVVJqpIxAIMC722/evCku09LSgk9fCgsLw4/gI2XQvx/DiuF3F6kRTACAPujmzZsIoenTp+Pf1tXVqaqqamtrczgccZn6+nqEkKurq+SJ+DjHAwcOSF1wz549SGYPCzabra2traurK74s/smknp5edXX1B4PEB8UMGzZM9qDsSBkymZyamipZcsuWLQiha9eutXV9/C8HFxeXD0aC/TtShsFgDGkNl8tt51x8VI7UJ59SDAwM1NXVpQ42NzebmJjQ6XSpt7Z9+3bJ2y4+UgYhtGHDBsliy5YtQwht2bKlI28QANCj4QMVIyMj8W9///13hNDatWsly+CPylIJHB+NLpXEhEKho6MjmUyWfJLHMAz/JHXNmjXiI/h8/ODg4I4Eifd6SO0019ZImeHDhwuFQsmSBgYG7T9j//rrrwih/fv3dyQY8UgZkUhka2vLYrHw6vCV7PFH6I6MlBEIBGQyuX///u2U2bp1K0Lo3LlzUsfxIatffvml5MGcnBwGgyGOB/t3pIyxsbHkwNLnz58jhExMTPCBQqAngt2XgIKRyWS880IMn03a8amYnfXpp58ihDq7jLmOjg6GYfHx8R8nqI7Cb5xdWIM9ISGhqKiof//+kqv/UiiUzZs3I4SuXLkiWbj7fygAgJ4CX3pgwYIF+LeamprTpk2rra2NiIjo2gXPnj2LEPrmm28kDxoaGgYEBFRXV+PPjmL4cL+uVdSWSZMmDRgwQPIIXgU+0VVRtfD5/IzWiESids7CI0lISGhpaelUdffv3y8tLZ06darUW/viiy8QQrdv35Y8SCKRNmzYIHkEvwVcvXq1U5UCAHocNpt9+/ZtIyMjfMdrhNDs2bPpdPpff/0l/qCuUxISErKyskaNGiU1WmT58uU0Gk0q+SCE8M4Fxdq4caPkCowIIW1t7fr6enxhGgUikUjz58/Pz89/9OgRQujUqVP4kQ6eTqFQ1NXVS0pK8CXDOuWvv/4ik8l4l42YtbX1pEmT8vPzpd7pihUrJEeMent7+/j4lJaWPnv2rLP1AiUB05eAgpmbm0sNLMcnwOPr18ovNzc3NDQ0Li6uuLi4trZWfBzfzK/jlixZcv/+/XHjxo0cOXLs2LFjxozx9vaWyvgKl5GRsWfPnsTExMLCQskt9zobPEIoMzMT/TtZQBI+40Aqd5uamkotPaPYHwoAoIeqqqqKjIzU1dWVXGNrwYIF58+fP3nyJD6hqVMEAkFGRgaVShXPAxLLyspCCOH7norJzr2Xn+w1AwMDd+zYsXv37uvXr0+ZMsXX13fMmDGSCyt2gb29/Zs3b9p6tampSapzfNSoUSwWa8KECaampufOnXvy5Im/v7+vr++4ceM6MvcW3/WjtLT0q6++knqJRqNJtaq+vr7UvFdHR0cmk/n27Vt8M+8PVgcA6KHOnDkjEAjmzZsnXkVLV1fX39//ypUrN2/exMdZdAqefOrr62WTD5PJxJdiFG8XzWQy8Z06FEt2abPFixdv3brVzc0Nf4YfO3as1K4gXbZo0aIdO3acPn3ay8vr6tWrvr6++PQiKffu3SspKRF/a2JiMm7cOBKJtHjx4t9++83JyWn8+PFjxowZN26cs7PzBytls9nFxcUaGhr4UFNJhYWFCKG8vDzJzQdll3Vzd3ePjY3NyMjo+EwroFSgUwYomOxjLt7T0f6Hhx2UmZk5fPjw+vp6Hx+fyZMn6+jokMnkioqKffv2CYXCTl1q7ty5ampqP//885MnTx49evTtt98aGxv/+OOPH2825rNnz8aOHSsQCHx9ff39/XV0dEgkUm5ublhYWGeDRwg1Njai1lY9wI9I9vigj/xDAQD0XGfOnOHxeI6OjvhIbxy+ehe+rRu+P1HH1dfX44kFX31Gio6OjlTa+RhLnMhe08rK6sWLF99+++3t27f379+/f/9+JpP5+eef//TTT+0vT9NllZWVUhu7/v333ywWS1tbOz4+/ptvvomIiDh8+PDhw4dpNNrs2bP379/fflPU1NQghF6+fCn7ybC6urrU6mCyK9OTSCQDA4OCgoLGxkaFD00CACiPU6dOIYRqa2t3794t9dKJEye60CmDJ5/MzEzZ4dX4wJCWlhZxV6++vr64g0aBZNPjV199ZWxsfOjQoaioKHzlYBsbm/379+PD5+XBYrFGjRoVHh7u5ubW2NjY1hbde/bsiY6OFn+L978ghPbt24evv3bjxg18abB+/fodPHjQz8+vnUrxj5m5XG5bt06pwZWySR5//sf/OgA9EXTKgJ7kp59+qq2tPXLkyIoVK8QHnzx5gs+M7axPP/30008/raysfPz4cWRk5Llz55YtW6apqRkYGNip6+C3H6m/NIRCYXNzs+Rqu99++y2Hw7l69er06dPFB8PDw8PCwroQPD7yBV+ORxI++KX9Pf8AAACHP76npaXh26NKOX36tNQspA9SV1cnkUhMJrOiooJCoSgkSIVwdnYODw9vbm5+/vx5dHT08ePHf/vtt6ampj///PNjVGdoaChe2REnHthobm5+6tSplpaWhISE+/fvnzhx4uzZs2VlZXfv3m3ngnjO37x5M77mQvtkbw0YhpWXl5PJZHV19c69EwBAz/H8+XN8JPXx48dlX71z504Xutrx5LNs2bIDBw58sPDH6JFpq6LFixcvXry4pKTk4cOHERERV65cmTFjRmxs7NChQ+W8+KJFi2JiYrZu3aquri5eJ1jKjz/+iK8CiRN3G1EolLVr165duzY/P//BgwdXr16NjIycPHlySkqKeAMpWXgjGxkZ4Tt5f1Bbz/9S4+JBDwKdMoB4eP96RybYp6amIoTmzJkjeTApKUme2vX19WfMmDFjxgw/P7/58+eHh4e31SmDxyk7qsXY2BjJTAV6//49n8+XCl5VVVWqC182+LZqkdK/f3/8dMlRowghfAVKySGOAADQqoSEhLS0NDs7O9k9ocvLy4OCgk6dOvX111+39ZCNj86QSlZ0Ot3FxSUjIyM1NVV2fiXhGAyGr6+vr69vSEgIi8W6cuUK3ilDpVJJJFJn13lpB5PJnDJlSjsFqFTq0KFDhw4dunbtWjs7u3v37tXV1WlpaSGEaDSa1GhH9O++IU+fPu1I7ZWVlQUFBZL7A2ZkZDQ3Nzs7O8PcJQB6MXyNsDVr1nzyySdSL50+ffqvv/7666+/8IXPW9XqA3mnkk/3MzU1nTt37ty5c3fu3Pn1119fu3YN75Rp9Q7VQTNnzly1alVDQ8PixYvb6siW3ZBbCovFwruN1q5de+DAgVu3buGdMq0+55uYmBgbGxcXF+fn57NYrA9GmJycLPkRL/r3Dwr8rwPQE8FCv4B42traampqJSUlH5xNg/dDFxQUiI80NDTgO4B0luwAP3wf7nZWQTM1NUX/zu2UZGNjg2TWWdy5c6dUMX19fS6XK9m3XVZWhu8V0pFapAwaNMjKyiojIwPfOQUnEAjwyaidHewDAOiD8Mf3efPmjZUxd+5cNze37Ozsx48ft3V6W8lq0aJFCKFvvvlGto+DqJHVsvXq6+urqKjweDx86V8VFRUDA4OysjKBQNDNkairq+ObDIrvPmZmZs3NzVJrjY0fP97U1PTevXv379/vyGWlBpDi33Zh5gIAoKfgcrmXLl0ik8kbN26Uzer4sI6TJ0+2s945ntWlBmsMGTLE2dk5KSnp8uXLsqcQktVFIhGHw5E6KPUY38HH6VapqanFxMRER0f/+OOPnTqRz+fL/h3RamBSjUwikfBb59atW2V/QLKNHBYWJtl3HxcX9+zZM3Nz8yFDhnQqYKA8YKQMUAqenp6PHj2aNWvWiBEjmEymvb19q3Mv/fz8Hj16NHfu3NDQUHyRxW3btklOEeo4FxcXPz8/f39/GxsbVVXVV69e4aP0Z82a1dYpXl5eCKE9e/bU1taamZmRSKRZs2Zpa2tPnDhRS0srPDx8/fr1+K4lZ86cSUxMZDAYUsG/efNm+vTpO3bsYLFYKSkp33zzjb6+Pr7XrFQtoaGh1dXVpqamJBLps88+wz8+lUShUPbt2xcQEDB//vwff/xx9OjRZWVle/bsSU5O9vT0nDdvXhfaBADQdzQ3N//9999IZuyhWFBQUEpKysmTJ0eNGtVqAQ8PDzKZfOzYMRqNxmKxKBTKlClTzMzMVq9efe3atdu3b48YMeKLL75wcXERCAR5eXn37t27evWqeM/m7rRy5cqSkpLZs2c7OTkZGxsXFhb+8ssvHA5n4cKF4nFAnp6e//zzT0BAgJ+fn6qqqrW1Nb5AQFvYbPaqVatkj48bN66dRQ127dp17969+fPn9+vXz9zcnM1m//HHH9nZ2b6+vuI1wjw9PRMSEgICAj755BMNDQ0DA4Pp06czGIxjx45NnTrV399/zZo1fn5+ZmZmJSUlr1+/Pnfu3KRJk3bs2CGuxcDA4NixY3Q6PSgoSCQSnThx4tSpU6ampuvXr+9K8wEAeoLLly/X1tb6+vq2OkHJ09PT0dExKysrLi7Ox8en1St4eXnduXNnwYIFgYGBWlpampqas2fPplAox48fHz169Ny5c2NjY8ePH29paclms7Oysi5cuDBw4EB8y+3u1NzcbGpqGhQUNHbsWBsbGyqVmpiYuHXrVhKJJP5U0t3dnUKhnDx5ksFgWFlZUSiUyZMnm5ubd7AKfN+MziouLvb29p4/f/6oUaNsbGwwDIuLiwsNDaXRaNOmTcPLeHp6IoT279/P4XDMzMzIZPLMmTP19PS+/vrryMjI8+fPl5WVLVu2zMnJicvl5ubm3r59Oy4uLjc3V7IiBoMxduzYH374wcrKKj4+ftOmTQihXbt2iVd3Bj0PQVtxg94Af5atr6/Hv21ubkYIOTk5SRXDB4fv3LlTfARfM1x8IoZhr1+/HjJkiHjzowULFuDH9fX18Y8QcVwuV2pAuI+Pz5MnTxBCkyZNEhfDPxI8dOhQO8HL9iWrqqqGhoaKC/zzzz8IoZCQEMmzdu3apaenJz4lMzM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"\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "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=(1500, 900))" ] }, { "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)\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", "$$\n", "F[q] = \\sum_{a=1}^M \\underbrace{\\sum_{x_a} \\left( \\prod_{j\\in N(a)} q_j(x_j)\\cdot \\left(-\\log p_a(x_a)\\right) \\right) }_{\\text{node energy }U[p_a]} - \\sum_{i=1}^N \\underbrace{\\sum_{x_i} q_i(x_i) \\log \\frac{1}{q_i(x_i)}}_{\\text{edge entropy }H[q_i]}\n", "$$\n", "\n", "- 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", "$$\n", "F_B[q] = \\sum_{a=1}^M \\left( \\sum_{x_a} q_a(x_a) \\log \\frac{q_a(x_a)}{p_a(x_a)} \\right) - \\sum_{i=1}^N (d_i - 1) \\sum_{x_i} q_i(x_i) \\log {q_i(x_i)}\n", "$$\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", "$$\n", " \\sum_{x_a \\backslash x_i} q_a(x_a) = q_i(x_i), ~~~ \\forall i, \\forall a \\\\\n", " \\sum_{x_i} q_i(x_i) = 1, ~~~ \\forall i \\\\\n", " \\sum_{x_a} q_a(x_a) = 1, ~~~ \\forall a \\\\\n", "$$\n", "\n", "- We form the Lagrangian by augmenting the Bethe Free Energy functional with the constraints:\n", "$$\n", "L[q] = F_B[q] + \\sum_i\\sum_{a \\in N(i)} \\lambda_{ai}(x_i) \\left(q_i(x_i) - \\sum_{x_a\\backslash x_i} q(x_a) \\right) + \\sum_{i} \\gamma_i \\left( \\sum_{x_i}q_i(x_i) - 1\\right) + \\sum_{a}\\gamma_a \\left( \\sum_{x_a}q_a(x_a) -1\\right)\n", "$$\n", "\n", "- The stationary solutions for this Lagrangian are given by\n", "$$\n", "q_a(x_a) = f_a(x_a) \\exp\\left(\\gamma_a -1 + \\sum_{i \\in N(a)} \\lambda_{ai}(x_i)\\right) \\\\ \n", "q_i(x_i) = \\exp\\left(1- \\gamma_i + \\sum_{a \\in N(i)} \\lambda_{ai}(x_i)\\right) ^{\\frac{1}{d_i - 1}}\n", "$$\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", "$$\n", "\\gamma_a = 1 - \\log \\Bigg(\\sum_{x_a}f_a(x_a)\\exp\\left(\\sum_{i \\in N(a)}\\lambda_{ai}(x_i)\\right)\\Bigg)\\\\\n", "\\gamma_i = 1 + (d_i-1) \\log\\Bigg(\\sum_{x_i}\\exp\\left( \\frac{1}{d_i-1}\\sum_{a \\in N(i)} \\lambda_{ai}(x_i)\\right)\\Bigg).\n", "$$\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", "$$ \\sum_{x_a \\backslash x_i} f_a(x_a) \\exp\\left(\\sum_{i \\in N(a)} \\lambda_{ai}(x_i)\\right) = \\exp\\left(\\sum_{a \\in N(i)} \\lambda_{ai}(x_i)\\right) ^{\\frac{1}{d_i - 1}} \\\\ \\exp\\left(\\lambda_{ai}(x_i)\\right)\\sum_{x_a \\backslash x_i} f_a(x_a) \\exp\\Bigg(\\sum_{\\substack{{j \\in N(a)} \\\\ j \\neq i}}\\lambda_{aj}(x_j)\\Bigg) = \\exp\\left(\\sum_{a \\in N(i)} \\lambda_{ai}(x_i)\\right) ^{\\frac{1}{d_i - 1}}\\\\ \\exp\\left(\\lambda_{ai}(x_i) + \\lambda_{ia}(x_i)\\right) = \\exp\\left(\\sum_{a \\in N(i)} \\lambda_{ai}(x_i)\\right) ^{\\frac{1}{d_i - 1}}\\,, $$ \n", "where we defined an auxilary function\n", "$$\n", "\\exp(\\lambda_{ia}(x_i)) \\triangleq \\sum_{x_a \\backslash x_i} f_a(x_a) \\exp\\Bigg(\\sum_{\\substack{{j \\in N(a)} \\\\ j \\neq i}}\\lambda_{aj}(x_j)\\Bigg) \\,.\n", "$$\n", "This definition is valid since it can be inverted by the relation\n", "$$\n", "\\lambda_{ia}(x_i) = \\frac{2-d_i}{d_i - 1}\\lambda_{ai}(x_i) + \\frac{1}{d_i -1}\\sum_{\\substack{c \\in N(i)\\\\c \\neq a}}\\lambda_{ci}(x_i)\n", "$$\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", "$$ \\mu_{ia}^{(k+1)}(x_i) = \\sum_{x_a \\backslash x_i} f_a(x_a) \\prod_{\\substack{{j \\in N(a)} \\\\ j \\neq i}}\\mu^{(k)}_{aj}(x_j) \\\\ \\mu_{ai}^{(k)}(x_i) = \\prod_{\\substack{c \\in N(i) \\\\ c \\neq a}}\\mu^{(k)}_{ic}(x_i)\\,, $$ \n", "where $k$ denotes iteration number and the messages are defined as\n", "$$\n", "\\mu_{ia}(x_i) \\triangleq \\exp(\\lambda_{ia}(x_i))\\\\\n", "\\mu_{ai}(x_i) \\triangleq \\exp(\\lambda_{ai}(x_i))\\,.\n", "$$\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": 8, "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 1.9.2", "language": "julia", "name": "julia-1.9" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.9.2" } }, "nbformat": 4, "nbformat_minor": 4 }