From Information Theory to Variational Inference

date_range 09/01/2019 infosort labelMachine Learning
Outline
Variational Inference $(\text{VI})$ are useful methods for approximating hardtocompute probabilisty densities. The main idea behind VI is that a target distribution $p$ of some dataset can be estimated by introducing an approximate distribution $q$, and then, iteratively minimizing the KullbackLeibler divergence $\text{KL}(qp)$ between $q$ and $p$. Many reinforcement learning algorithms, e.g., variational inference for policy search, aim to optimize the policy by minimizing the KLdivergence between a policy distribution and an improper rewardweighted distribution. This post discussse the following topics that are basic, but important, concepts to understanding VI.
 Information Theory
 Information
 Entropy
 KullbackLeibler divergence
 Statistics
 Jensen’s inequality
 Evidence lower bound $(\text{ELBO})$
 Graphic models
 Bayesian Networks
and then, talk about
 Varitional Inference
1. Information Theory
Information
One of the core basic concept in information theory is “Information”. The amount of “information” contains in an event $x$ is defined formally $(\text{or mathematically})$ as
where $p(x)$ is the occurrence probability of event $x$. Informally, the more one knows about an event $(\text{high probability})$, the less hidden information he is apt to get about it $(\text{less information})$.
 For example, the probability of a dice being a particular number, e.g., 3, is 1/6. Thus, the information of a dice rolling is $I(x) = \log_{2}{\frac{1}{1/6}} = \log_{2}{6}=2.58$ bits. On the other hand, the probability of a coin being head or tail is $1/2$, and hence, the information of a coin toss is $I(x) = \log_{2}{\frac{1}{1/2}}=1$ bit.

Event Probability Information Coin toss 1/2 $I(X)=\log_{2}{\frac{1}{1/2}}=1$ bit Dice rolling 1/6 $I(X)=\log_{2}{\frac{1}{1/6}} = 2.58$ bits
Entropy
“Entropy” measures the average “Information” of the source data. Shannon defined the entropy $(H)$ of a set of discrete random variable $X={x_1, x_2, x_3, \cdots, x_n}$ with probability mass function $P(X)$ explicitly as
where $b$ is the base of the logarithm, e.g., $b=2$, $b=10$, or $b=e$.
 Discrete variables $X=[10, 20, 30, 40]$ with equal probability $p(x_{i})=\frac{1}{4}$
 Continuous variables $X \in \mathbb{R}$ with probabilty density function of the exponential distribution $p(x)=\lambda e^{\lambda x}$
KullbackLeibler $(\text{KL})$divergence
The KullbackLeibler divergence $(\text{also named relative entropy})$ was first introduced by Solomon Kullback and Richard Leibler in 1951 as the directed divergence between two distributions. In statistics, the KLdivergence is commonly used to measure how one probability distribution different from a second, reference probability distribution.
For discrete probability distributions, $P$ and $Q$ are defined on a same probability space, the KLdivergence between $P$ and $Q$ is defined as $(\text{see below})$. The KLdivergence is also interpreted as the mean of the logarithm difference between two distributions, where the expectation is taken using the probability $P$.
For continuous probability distributions, $P$ and $Q$, the KLdivergence is defined as an integral $(\text{see below})$.
 Example: Calculate the KLdivergence in discrete domain between $p$ and $q$, and the KLdivergence between $p$ and $t$?
 Example: Calculate the KLdivergence between two normal distributions, $p \sim \mathcal{N}(\mu_1, \sigma_1^2)$ and $q \sim \mathcal{N}(\mu_2, \sigma_2^2)$.
Some properties of the KLdivergence are
 It is Nonnegative: $D_{KL}(P  Q) \geq 0 $,
 It is asymmetric. $D_{KL}(P  Q) \neq D_{KL}(QP)$,
 It is invariant under parameter transformation. $\text{(this property turns out very useful in machine learning or reinforcement learning, e.g., natural gradient)}$.
2. Statistics
Jensen's inequality
Jensen’s inequality generalizes the statement that a secant line of a convex function lies above the graph $(\text{Wikipedia})$. Let $f(x)$ to be a real continuous function $(\text{convex or concave})$, thus the Jensen’s inequality is
In the domain of probability theory, if the $p_1, p_2, \cdots, p_n$ are positive number that sum to 1, and $f(x)$ is a convex function, then
On the other hand, if $f(x)$ is a concave function, then
Evidence Lower Bound $(\text{ELBO})$
Now, let’s start from the log probability of a random variable $X$. Here, $f(x)=\log{(x)}$ is a concave function. Thus, we can have
We denote $L=E_z [\log p(X, Z)] + H(Z)$ as the Evidence Lower Bound $(\text{ELBO})$, where $H(Z)= E_z [\log(q(Z))]$ is the Shannon entropy. The $q(Z)$ in the equation is a distribution used to approximate the true posterior distribution $p(ZX)$ in VI. Maximizing the ELBO gives as tight a bound on the log probability. Or, if we want to maximize the marginal probability, we can instead maximize its ELBO $L$.
3. Graphical Models
Probability theory plays a crucial role in modern machine leanring. Graphical models provide a simple and elegant way to represent the structure of a probabilitic model, show insights into the properties of the mdoel, especially conditional independence properties. Generally, a graph consists of nodes and links, where each node represent a random variable and the links express probabilistic relationships between these variables. The graph then captures the way in which the joint distribution over all the random variables can be decomposed into a product of factors each depending only on a subset of the variables.
Bayesian Networks
The graph is a directed graphical model which is typically used to describe probability distribution in Bayesian inference. The graphical model represents the joint probability distribution over three variables $A$, $B$ and $C$. We can therefore write the joint distribution in the form
4. Variational Inference
Finally, we are now ready to introduce the Variational Inference.
Problem Setup
Assume that $X$ are observations $(\text{data})$ and $Z$ are hidden variables, the hidden variables might include the “parameters”. The relationship of these two variabels can be represented using a grapical model
The goal of variational inference is to infer hidden variables from observations, that is we want the posterior distribution
where the joint probability $p(X, Z)$ is generally easy to compute, however, the marginal probability $p(X)=\int_Z p(X,Z) dz$ is intractable in most cases.