{ "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", " - 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", " - Optional \n", " - Bishop (2016), pp. 461-486 (sections 10.1, 10.2 and 10.3) \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", " - Zhang et al. (2017), [Unifying Message Passing Algorithms Under the Framework of Constrained Bethe Free Energy Minimization](https://github.com/bertdv/BMLIP/blob/master/lessons/notebooks/files/Zhang-2017-Unifying-Message-Passing-Algorithms.pdf)\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", " - Caticha (2010), [Entropic Inference](https://arxiv.org/abs/1011.0723)\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 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", " " ] }, { "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 constrast to parameters). These observation-dependent variables are generally a useful tool to encode structure in the model. 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 very complex models. Unfortunately, inference in latent variable models can also be much more complex than for fully observed models." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Inference for GMM is Difficult\n", "\n", "\n", "- We recall here the log-likelihood for the Gaussian-Categorial Model, see [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 given, this expression decomposed into a set of simple updates for the Gaussian and multinomial distributions. For both distributions, we have conjugate priors, so inference is easily solved. " ] }, { "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 need to estimated alongside with the parameters." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- There is no conjugate prior on the latent variables for the GMM likelihood function $p(\\underbrace{D}_{\\{x_n\\}}\\,|\\,\\underbrace{\\{z_{nk}\\},\\{\\mu_k,\\Sigma_k,\\pi_k\\}}_{\\text{all latent variables}})$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- 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": [ "- As a motivation for variational inference, we first discuss inference by the **Method of Maximum Relative Entropy**, [(Caticha, 2010)](https://arxiv.org/abs/1011.0723). " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Inference When Information is in the Form of Constraints \n", "\n", "- In the [probability theory lesson](https://nbviewer.jupyter.org/github/bertdv/BMLIP/blob/master/lessons/notebooks/Probability-Theory-Review.ipynb#Bayes-rule), we recognized Bayes rule as the fundamental rule for learning from data." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- We will now generalize this notion and consider learning as a process that updates a prior into a posterior distribution whenever new information becomes available." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- In this context, *new information* is not necessarily a new observation, but could (for instance) also relate to *constraints* on the posterior distribution." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- For example, consider a model $\\prod_n p(x_n,z) = p(z) \\prod_n p(x_n|z)$. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Our prior beliefs about $z$ are represented by $p(z)$. In the following, we will write $q(z)$ to denote a posterior distribution for $z$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- We might be interested in obtaining rational posterior beliefs $q(z)$ in consideration of the following additional constraints:\n", " 1. *Data Constraints*: e.g., two observed data points $x_1=5$ and $x_2=3$, which lead to constraints $q(x_1) = \\delta(x_1-5)$ and $q(x_2)=\\delta(x_2-3)$.\n", " 2. *Form Constraints*: e.g., we only consider the Gamma distribution for $q(z) = \\mathrm{Gam}(z|\\alpha,\\beta)$.\n", " 3. *Factorization Constraints*:, e.g., we consider independent marginals for the posterior distribution: $q(z) = \\prod_k q(z_k)$. \n", " 3. *Moment Constraints*: e.g., the first moment of the posterior is given by $\\int z q(z) \\mathrm{d}z = 3$.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- Note that this is not \"just\" applying Bayes rule to compute a posterior, which can only deal with data constraints as specified by the likelihood function." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Note also that observations _can_ be rephrased as constraints on the posterior, e.g., observation $x_1=5$ can be phrased as a constraint $q(x_1)=\\delta(x_1-5)$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- $\\Rightarrow$ Updating a prior to a posterior on the basis of constraints on the posterior is more general than updating based on observations alone." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### The Method of Maximum Relative Entropy" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- [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)) developed the **method of maximum (relative) entropy** for rational updating of priors to posteriors when faced with new information in the form of constraints." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- Consider prior beliefs $p(z)$ about $z$. New information in the form of constraints is obtained and we are interested in the \"best update\" to a posterior $q(z)$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- In order to define what \"best update\" means, we assume a ranking function $S[q,p]$ that generates a preference score for each candidate posterior $q$ for a given $p$. The best update from $p$ to $q$ is identified as $q^* = \\arg\\max_q S[q,p]$. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Similarly to [Cox' method to deriving probability theory](https://en.wikipedia.org/wiki/Cox%27s_theorem), Caticha introduced the following mild criteria 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", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- These three criteria **uniquely identify the Relative Entropy** (RE) as the proper ranking function: \n", "\\begin{align*}\n", "S[q,p] = - \\sum_z q(z) \\log \\frac{q(z)}{p(z)}\n", "\\end{align*}" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- $\\Rightarrow$ When information is supplied in the form of constaints on the posterior, we *should* select the posterior that maximizes the Relative Entropy, subject to the constraints. This is the **Method of Maximum (Relative) Entropy** (MRE). " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Relative Entropy, KL Divergence and Free Energy\n", "\n", "- Note that the Relative Entropy is technically a *functional*, i.e., a function of function(s) (since it is a function of probability distributions). The calculus of functionals is also called **variational calculus**.\n", " - See [Lanczos, The variational principles of mechanics (1961)](https://www.amazon.com/Variational-Principles-Mechanics-Dover-Physics/dp/0486650677) for a great book on variational calculus. For a summary, see Bishop (2016), app.D.\n", " - It is customary to use square brackets (like $S[q,p]$) for functionals rather than round brackets, which we use for functions. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Also note the complimentary relation between the Maximum Relative Entropy method and Probability Theory: \n", " - PT describes how to _represent_ beliefs about events and how to _calculate_ beliefs about joint and conditional events. \n", " - In contrast, the MRE method describes how to _update_ beliefs when new information becomes available.\n", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- PT and the MRE method are both needed to describe the theory of optimal information processing. (As we will see below, Bayes rule is a special case of the MRE method.)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- In principle, entropies can always be considered as a *relative* score against a reference distribution. For instance, the score $-\\sum_{z_i} q(z_i) \\log q(z_i)$ can be interpreted as a score against the uniform distribution, i.e., $-\\sum_{z_i} q(z_i) \\log q(z_i) \\propto -\\sum_{z_i} q(z_i) \\log \\frac{q(z_i)}{\\mathrm{Uniform(z_i)}}$. Therefore, the \"method of maximum relative entropy\" is often simply known as the \"method of maximum entropy\". " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- The negative relative entropy is known as the **Kullback-Leibler** (KL) divergence:\n", "$$\n", "\\mathrm{KL}[q,p] \\triangleq \\sum_z q(z) \\log \\frac{q(z)}{p(z)} \\tag{B-1.113}\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- The [Gibbs inequality](https://en.wikipedia.org/wiki/Gibbs%27_inequality) ([proof](https://en.wikipedia.org/wiki/Gibbs%27_inequality#Proof)), is a famous theorem in information theory that states that \n", "$$\\mathrm{KL}[q,p] \\geq 0$$\n", "with equality only iff $p=q$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ " - The KL divergence can be interpreted as a \"distance\" between two probability distributions. Note however that the KL divergence is an asymmetric distance measure, i.e., in general $\\mathrm{KL}[q,p] \\neq \\mathrm{KL}[p,q]$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- We also introduce here the notion of a (variational) **Free Energy** (FE) functional, which is just a generalization of the KL-divergence that allows the prior to be unnormalized. Let $f(z) = Z \\cdot p(z)$ be an unnormalized distribution, then the FE is defined as\n", "\\begin{align*}\n", "F[q,p] &\\triangleq \\sum_z q(z) \\log \\frac{q(z)}{f(z)} \\\\\n", "&= \\sum_z q(z) \\log \\frac{q(z)}{Z\\cdot p(z)} \\\\\n", "&= \\underbrace{\\sum_z q(z) \\log \\frac{q(z)}{p(z)}}_{\\text{KL divergence }\\geq 0} - \\log Z\n", "\\end{align*}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Since the second term ($\\log Z$) is constant, FE minimization (subject to constraints) with respect to $q$ leads to the same posteriors as KL minimization and RE maximization." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- If we are only interested in minimizing FE with respect to $q$, then we'll write $F[q]$ (rather than $F[q,p]$) fo brevity. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- In this class, we prefer to discuss inference in terms of minimizing Free Energy (subject to the constraints) rather than maximizing Relative Entropy, but note that these two concepts are equivalent. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Example KL divergence for Gaussians\n", "\n", "

\n", "\n", "source: By Mundhenk at English Wikipedia, CC BY-SA 3.0, Link\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### The Free Energy Functional and Variational Bayes\n", "\n", "- Let's get back to the issue of doing inference for models with latent variables (such as the GMM). " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Consider a generative model specified by $p(x,z)$ where $x$ and $z$ represent the observed and unobserved variables, respectively." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Assume further that $x$ has been observed as $x=\\hat{x}$ and we are interested in performing inference, i.e., we want to compute the posterior $p(z|x=\\hat{x})$ for the latent variables and we want to compute the evidence $p(x=\\hat{x})$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- According to the Method of Maximum Relative Entropy, in order to find the correct posterior $q(x,z)$, we should minimize\n", "\n", "$$\n", "\\mathrm{F}[q] = \\sum_{x,z} q(x,z) \\log \\frac{q(x,z)}{p(x,z)}\\,, \\quad \\text{subject to data constraint }x=\\hat{x}\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- The data constraint $x=\\hat{x}$ fixes posterior $q(x) = \\delta(x-\\hat{x})$, so the Free Energy evaluates to \n", "\n", "\\begin{align*}\n", "F[q] &= \\sum_{z} \\sum_{x}q(z|x)q(x) \\log \\frac{q(z|x) q(x)}{p(z|x) p(x)} \\\\\n", " &= \\sum_{z} \\sum_{x} q(z|x)\\delta(x-\\hat{x}) \\log \\frac{q(z|x)\\delta(x-\\hat{x})}{p(z|x) p(x)} \\\\\n", " &= \\sum_{z} q(z|\\hat{x}) \\log \\frac{q(z|\\hat{x})}{p(z|\\hat{x}) p(\\hat{x}) } \\\\\n", " &= \\underbrace{\\sum_{z}q(z|\\hat{x}) \\log \\frac{q(z|\\hat{x})}{p(z|\\hat{x})}}_{\\text{KL divergence }\\geq 0} - \\underbrace{\\log p(\\hat{x}) }\\tag{B-10.2\n", "}_{\\text{log-evidence}}\\end{align*}" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- The log-evidence term does not depend on $q$. Hence, the global minimum $q(z|\\hat{x})^* \\triangleq \\arg\\min_q F[q]$ is obtained for $$q^*(z|\\hat{x}) = p(z|\\hat{x})\\,,$$ which is the correct **Bayesian posterior**." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Furthermore, the minimal free energy $$F^* \\triangleq F[q^*] = -\\log p(\\hat{x})$$ equals minus **log-evidence**. (Or, equivalently, the evidence is given by $p(\\hat{x}) = \\exp\\left(-F[q^*] \\right)$.) " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "
\n", "
\n", "$\\Rightarrow$ Bayesian inference can be executed by FE minimization.\n", "
\n", "
\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- This is an amazing result! Note that FE minimization transforms an inference problem (that involves integration) to an optimization problem! Generally, optimization problems are easier to solve than integration problems. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Executing inference by minimizing the variational FE functional is called **Variational Bayes** (VB) or variational inference. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- (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. We prefer to discuss variational inference in terms of a free energy (but it is the same story as he discusses with the ELBO). " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### FE Minimization as Approximate Bayesian Inference\n", "\n", "- In the rest of this lesson, we are concerned with how to execute FE minimization (FEM) w.r.t $q$ for the functional\n", "$$\n", " F[q] = \\sum_{z}q(z) \\log \\frac{q(z)}{p(z|x)} - \\log p(x) \n", "$$\n", "where $x$ has been observed and $z$ represent all latent variables.\n", " - To keep the notation uncluttered, in the following we write $x$ rather than $\\hat{x}$, and we write simply $q(z)$ (rather than $q(z|\\hat{x})$) for the posterior. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Due to restrictions in our optimization algorithm, we are often unable to fully minimize the FE to the global minimum $q^*(z)$, but rather get stuck in a local minimum $\\hat{q}(z)$. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Note that, since $\\mathrm{KL}[q(z),p(z|x)]\\geq 0$ for any $q(z)$, the FE is always an upperbound on (minus) log-evidence, i.e.,\n", "$$\n", "F[q] \\geq -\\log p(x) \\,.\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- In practice, even if we cannot attain the global minimum, we can still use the local minimum $\\hat{q}(z) \\approx \\arg\\min_q F[q]$ to accomplish **approximate Bayesian inference**: " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "\\begin{align*}\n", " \\text{posterior: } \\hat{q}(z) &\\approx p(z|x) \\\\\n", " \\text{evidence: } p(x) &\\approx \\exp\\left( -F[\\hat{q}]\\right) \n", " \\end{align*}" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Constrained FE Minimization" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- Generally speaking, it can be very helpful in the FE minimization task to add some additional constraints on $q(z)$ (i.e., in addition to the data constraints)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Once we add constraints to $q$ (in addition to data constraints), we are no longer guaranteed that the minimum of the (constrained) FE coincides with the solution by Bayes rule (which only takes data as constraints). So again, at best constrained FEM is only an **approximation to Bayes rule**." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- There are three important cases of adding constraints to $q(z)$ that often alleviates the FE minimization task:\n", "\n", "#### 1. mean-field factorization\n", "- We constrain the posterior to factorize into a set of _independent_ factors, i.e., \n", "$$\n", "q(z) = \\prod_{j=1}^m q_j(z_j)\\,, \\tag{B-10.5}\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "#### 2. fixed-form parameterization\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. \n", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "#### 3. the Expectation-Maximization (EM) algorithm\n", "- We place some constraints both on the prior and posterior for $z$ ([to be discussed in the Optional Slides](#EM-Algorithm)) that simplifies FE minimization to maximum-likelihood estimation. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### FE Minimization with Mean-field Factorization Constraints: the CAVI Approach " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- 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", " - In other words, the posteriors for $z_j$ are all considered independent. This is a strong constraint but leads often to good solutions." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- 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 $q_{-j}^*(z_{-j}) \\triangleq q_1^*(z_1)q_2^*(z_2)\\cdots q_{j-1}^*(z_{j-1})q_{j+1}^*(z_{j+1})\\cdots q_m^*(z_m)$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- **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)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ " \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 $q_j^*(z_j)$. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- 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": { "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 \\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", "with tuning parameters $\\theta=\\{\\pi_k, \\mu_k,\\Lambda_k\\}$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Let us introduce some priors for the parameters. We factorize the prior and choose conjugate distributions by\n", "$$\n", "p(\\theta) = p(\\pi,\\mu,\\Lambda) = p(\\pi) p(\\mu|\\Lambda) p(\\Lambda)\n", "$$\n", "with \n", "\\begin{align*}\n", "p(\\pi) &= \\mathrm{Dir}(\\pi|\\alpha_0) = C(\\alpha_0) \\prod_k \\pi_k^{\\alpha_0-1} \\tag{B-10.39}\\\\\n", "p(\\mu|\\Lambda) &= \\prod_k \\mathcal{N}\\left(\\mu_k | m_0, \\left( \\beta_0 \\Lambda_k\\right)^{-1} \\right) \\tag{B-10.40}\\\\\n", "p(\\Lambda) &= \\prod_k \\mathcal{W}\\left( \\Lambda_k | W_0, \\nu_0 \\right) \\tag{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)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- 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 $\\{ \\alpha_0, m_0, \\beta_0, W_0, \\nu_0\\}$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "#### inference\n", "\n", "- Assume that we have observed $D = \\left\\{x_1, x_2, \\ldots, x_N\\right\\}$ and are interested to infer the posterior distribution for the tuning parameters: \n", "$$\n", "p(\\theta|D) = p(\\pi,\\mu,\\Lambda|D)\n", "$$" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- The (perfect) Bayesian solution is \n", "$$\n", "p(\\theta|D) = \\frac{p(x=D,\\theta)}{p(x=D)} = \\frac{\\sum_z p(x=D,z,\\pi,\\mu,\\Lambda)}{\\sum_z \\sum_{\\pi} \\iint p(x=D,z,\\pi,\\mu,\\Lambda) \\,\\mathrm{d}\\mu\\mathrm{d}\\Lambda}\n", "$$\n", "but this is intractable (See [Blei (2017), p861, eqs. 8 and 9](https://www.tandfonline.com/doi/full/10.1080/01621459.2017.1285773))." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Alternatively, we can use **FE minimization with factorization constraint** \n", "$$\$$\n", "q(\\theta) = q(z) \\cdot q(\\pi,\\mu,\\Lambda) \\tag{B-10.42}\n", "\$$$$ \n", "on the posterior. For the specified model, this leads to FE minimization wrt the hyperparameters, i.e., we need to minimize the function $F(\\alpha_0, m_0, \\beta_0, W_0, \\nu_0)$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- Bishop shows that the equations for the [optimal solutions (Eq. B-10.9)](#optimal-solutions) are analytically solvable for the GMM as specified above, 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", "" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "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": { "slideshow": { "slide_type": "slide" } }, "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, e.g., [(B-10.42)](#mf-constraint). " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "using Pkg;Pkg.activate(\"probprog/workspace\");Pkg.instantiate()\n", "IJulia.clear_output();" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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", 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