# Exponential Family

tags
Statistics

A one-parameter exponential family model is any model whose density can be expressed as:

$$p(y | \theta)=h(y) g(\theta) \exp \{\eta(\theta) t(y)\}$$

where $$\theta$$ is the parameter of the family, and $$t(y)$$ is the sufficient statistic for $$\theta$$.

When a model belongs to the one-parameter exponential family, a family of conjugate prior distributions is given by:

$$p(\theta) \propto g(\theta)^{\nu} \exp \{\eta(\theta) \tau\}$$

where $$\nu$$ and $$\tau$$ are parameters of the prior, such that $$p(\theta)$$ is a well-defined pdf.

Combining this prior with a sampling model $$Y \sim p(y|\theta)$$ yields the posterior:

\begin{align} p(\theta | y) & \propto p(y | \theta) p(\theta) \ & \propto g(\theta) \exp \{\eta(\theta) t(y)\} \cdot g(\theta)^{\nu} \exp \{\eta(\theta) \tau\} \ & \propto g(\theta)^{\nu+1} \exp \{\eta(\theta)[\tau+t(y)]\} \end{align}

which belongs to the same family as the prior distribution, with parameters $$\nu + 1$$ and $$\tau + t(y)$$.