# The Exponential Family: Getting Weird Expectations!

I spent quite some time delving into the beauty of variational inference in the recent month. I did not realize how simple and convenient it is to derive the expectations of various forms (e.g. logarithm) of random variables under variational distributions until I finally got to understand (partially, ) how to make use of properties of the exponential family.

In the following, I would like to share just enough knowledge of exponential family, which can help us obtain weird expectations that we may encounter in probabilistic graphical models. For complete coverage, I list some online materials that’s very helpful when I started to learn about these things.

### Definition

We say a random variable $x$ follows an exponential family of distribution if the probabilistic density function can be written in the following form:

$$\label{eq:pdf} p(x\vert \eta) = h(x) exp\big\{ \eta^T T(x) - A(\eta) \big\}$$

where:

• $\eta$ is a vector of parameters
• $T(x)$ is the sufficient statistics
• $A(\eta)$ is the cumulant function

The presence of $T(x)$ and $A(\eta)$ actually confuses me a lot when I first start to read materials on exponential family of distributions. It turns out that they can be understood by getting a little bit deeper into this special formulation of density functions.

### Fanscinating properties

We start by focusing on the property of $A(\eta)$. According to the definition of probabilities, the integral of density function in Equation \eqref{eq:pdf} over $x$ equals to 1:

\begin{align} \int p(x\vert \eta) dx &= exp(-A(\eta)) \int h(x)exp\{\eta^T T(x)\} dx = 1 \nonumber \\ A(\eta) &= log~\int h(x)exp\{\eta^T T(x)\} dx \label{eq:a_eta} \end{align}

From the equation above, we can tell that $A(\eta)$ can be viewed as the logarithm of the normalizer. Its value depends on $T(x)$ and $h(x)$.

An interesting thing happens now - if we take the derivative of Equation \eqref{eq:a_eta} with respect to $\eta$, we have:

\begin{align} \dfrac{\partial A(\eta)}{\partial \eta} &= \dfrac{\partial}{\partial \eta} log~\int h(x)exp\{\eta^T T(x)\} dx \nonumber \\[4pt] &= \dfrac{\int h(x) exp\{ \eta^T T(x) \} T(x) dx}{\int h(x) exp\{\eta^T T(x)\}dx } \nonumber \\[4pt] &= \dfrac{\int h(x) exp\{ \eta^T T(x) \} T(x) dx}{exp{A(\eta)}} \nonumber \\[4pt] &= \int h(x) exp\{ \eta^T T(x) - A(\eta) \} T(x) dx \nonumber \\[4pt] &\big[ \text{Note that } p(x\vert \eta) = h(x) exp\{ \eta^T T(x) - A(\eta) \} \text{ is a density function}\big] \nonumber \\[4pt] &= E\big[T(x)\big] \label{eq:e_t} \end{align}

How neat it is! It turns out the first derivative of the cumulant function $A(\eta)$ is actually the expectation of the sufficient statistic $T(x)$! If we go further - take the second derivative - we will get the variance of $T(x)$:

\begin{align} \dfrac{\partial A(\eta)}{\partial \eta \partial \eta^T} &= \dfrac{\partial}{\partial \eta^T} \int h(x) exp\{ \eta^T T(x) - A(\eta) \} T(x) dx \nonumber \\[4pt] &= \int h(x) exp\{ \eta^T T(x) - A(\eta) \} T(x) (T(x) - A'(\eta)) dx \nonumber \\[4pt] &= \int p(x\vert \eta) T^2(x) dx - A'(\eta) \int p(x\vert \eta) T(x) dx \nonumber \\[4pt] &= E\big[T^2(x)\big] - E\big[T(x)\big] E\big[T(x)\big] \nonumber \\[4pt] &= V\big[T(x)\big] \label{eq:v_t} \end{align}

In fact, we can go deeper and deeper to generate higher order moment of $T(x)$.

### Examples

Let’s end by taking a look at some familar probability distributions that belong to the exponential family.

#### Dirichlet distribution

Suppose a random variable $\theta$ is drawn from a Dirichlet distribution parameterized by $\alpha$, we have the following density function:

\begin{align*} p(\theta\vert \alpha) &= \dfrac{\Gamma(\sum_k \alpha_k)}{\prod_k \Gamma(\alpha_k)} \prod_k \theta_k^{\alpha_k-1} \\[4pt] &= exp\Big\{\ \sum_k(\alpha_k-1) log~\theta_k - \big[ \sum_k log~\Gamma(\alpha_k) - log~\Gamma(\sum_k \alpha_k) \big] \Big\} \end{align*}

This simple transformation turns Dirichlet density function into the form of exponential family, where:

• $\eta = \alpha - 1$
• $A(\eta) = \sum_k log~\Gamma(\alpha_k) - log~\Gamma(\sum_k \alpha_k)$
• $T(\theta) = log~\theta$

Such information is helpful when we want to compute the expectation of the log of a random variable that follows a Dirichlet distribution (e.g., this happens in the derivation of latent Dirichlet allocation with variational inference.)

\begin{align*} E\big[ log~\theta_k \big] &= E\big[ T_k(\theta) \big] = \dfrac{\partial}{\partial \eta_k} A(\eta) \\ &= \Psi(\alpha_k) - \Psi(\sum_j\alpha_j) \end{align*}

where $\Psi(\cdot)$ is the first derivative of log gamma. It is called the digamma function.

#### Gamma distribution

Gamma distribution has two parameters: $\alpha$ that controls the shape and $\beta$ that controls the scale. Therefore, we can find two sets of $A(\eta)$ and $T(x)$. Suppose $x \sim Gamma(\alpha, \beta)$, we have:

\begin{align*} p(x\vert \alpha, \beta) &= \dfrac{\beta^{\alpha}}{\Gamma(\alpha)} x^{\alpha-1} exp\{-\beta x\} \\ &= x^{\alpha-1} exp\bigg\{ -\beta x - \big[ log~\Gamma(\alpha) - \alpha log~\beta \big] \bigg\} \\ &= exp\{-\beta x\} exp\bigg\{ (\alpha-1)log~x - \big[ log~\Gamma(\alpha) - \alpha log~\beta \big] \bigg\} \end{align*}

Although it is obvious that $A(\eta) = log~\Gamma(\alpha) - \alpha log~\beta$ for both situations, the natural parameter $\eta$ is different. The first transformation helps us get the expectation of $x$ itself:

$$E[x] = \dfrac{\partial }{\partial -\beta} \bigg( log~\Gamma(\alpha) - \alpha log~\beta \bigg) = \dfrac{\alpha}{\beta}$$

Similarly, the second one helps get the expectation of $log~x$

$$E\big[log~x\big] = \dfrac{\partial }{\partial (\alpha-1)} \bigg( log~\Gamma(\alpha) - \alpha log~\beta \bigg) = \Psi(\alpha) - log~\beta$$

### Conclusion

In fact, there are many more to explore and know about the exponential family! Important concepts such as convexity and sufficency are not discussed here. Finally, I would recommend the following excellent materials for getting to know this cool concept of unifying a set of probabilistic distributions:

• Chapter 4.2.4 and 10.4 of Pattern Recognition and Machine Learning
• http://www.cs.columbia.edu/~jebara/4771/tutorials/lecture12.pdf