Exponential family

One-line definition

A distribution is in the exponential family if its density / mass function can be written as:

$$p(x\mid \theta )=h(x)\exp (\eta (\theta {)}^{\top }T(x)-A(\theta ))$$

with natural parameter $\eta (\theta )$, sufficient statistic $T(x)$, base measure $h(x)$, and log-partition $A(\theta )$ (which normalizes).

Why it matters

Most distributions you use day-to-day are exponential family: Gaussian, Bernoulli, categorical, Poisson, Beta, Gamma, Dirichlet, geometric, exponential. Recognizing them as such gives you free results:

• MLE is closed-form when the natural parameter is unconstrained: just match sample moments to model moments.
• Conjugate priors exist and are themselves exponential family.
• Sufficient statistics $T(x)$ contain all the data’s information about $\theta$. You can summarize a dataset by ${\sum }_{i}T(x_i)$ and forget the rest.
• Generalized linear models (GLMs) are linear regression generalized to exponential-family responses.

The canonical form

Given the form above:

• $T(x)$ are the sufficient statistics (Bernoulli: $T(x)=x$; Gaussian: $T(x)=(x,x^2)$).
• $\eta (\theta )$ are the natural parameters (Bernoulli: $\eta =\log \frac{p}{1-p}$, the logit; Gaussian: $\eta =(\mu /{\sigma }^2,-1/(2{\sigma }^2))$).
• $A(\theta )$ is the log-partition function; gradient gives the mean, Hessian gives the covariance of $T(x)$:

$$\nabla A(\theta )=\mathbb{E}[T(X)],{\nabla }^{2}A(\theta )=\mathrm{Cov}(T(X)).$$

This is why MLE via moment-matching works: the gradient of the log-likelihood is “data sufficient stat minus model expected sufficient stat.”

Common members

Distribution	Sufficient stat $T(x)$	Natural parameter $\eta$
Bernoulli($p$)	$x$	$\log (p/(1-p))$ (logit)
Categorical($\pi$)	one-hot$(x)$	$\log \pi$ (log-probabilities)
Gaussian($\mu ,{\sigma }^2$)	$(x,x^2)$	$(\mu /{\sigma }^2,-1/(2{\sigma }^2))$
Poisson($\lambda$)	$x$	$\log \lambda$
Beta($\alpha ,\beta$)	$(\log x,\log (1-x))$	$(\alpha -1,\beta -1)$
Gamma($\alpha ,\beta$)	$(\log x,x)$	$(\alpha -1,-\beta )$

Generalized linear models

A GLM combines a linear predictor $\eta =X\beta$ with an exponential-family response distribution. The link function maps the linear predictor to the natural parameter:

Response	GLM	Link
Continuous (Gaussian)	linear regression	identity
Binary (Bernoulli)	logistic regression	logit
Count (Poisson)	Poisson regression	log
Categorical	multinomial logistic	softmax
Time-to-event (Exponential, Weibull)	survival models	log

Logistic regression is a GLM with Bernoulli response and logit link. This unifies the entire family of “regression-style” classifiers.

Properties to remember

• Convexity: $A(\eta )$ is convex in $\eta$. So negative log-likelihood is convex, and there is a unique MLE.
• Sufficient statistics: by Pitman-Koopman-Darmois theorem, exponential families are essentially the only distributions with finite-dimensional sufficient statistics independent of $n$.
• Conjugacy: exponential families have conjugate priors, also in the exponential family.
• Maximum entropy: the exponential family with sufficient statistics $T$ matching given moments is the maximum-entropy distribution under those constraints.

Common pitfalls

• Forgetting that the Cauchy distribution is not exponential family. Heavy tails break the sufficient-statistic property.
• Confusing “exponential” (the distribution) with “exponential family” (the class). The Exp($\lambda$) distribution is one member.
• Treating natural parameters as the same as canonical parameters. A Gaussian’s natural parameters are $(\mu /{\sigma }^2,-1/(2{\sigma }^2))$, not $(\mu ,{\sigma }^2)$. Some software libraries default to one or the other; check.