# Bayesian parameter estimation in exponential families

(30 minutes to learn)

## Summary

Exponential families are convenient for Bayesian parameter estimation because the conjugate priors often have a convenient form, and there is a simple form for the posterior.

## Context

This concept has the prerequisites:

## Goals

- How do you derive the conjugate prior for an exponential family distribution?

- Show that the posterior can be computed in terms of the sufficient statistics.

- Work through a simple example, such as the beta-Bernoulli model.

## Core resources (read/watch one of the following)

## -Paid-

→ Machine Learning: a Probabilistic Perspective

A very comprehensive graudate-level machine learning textbook.

Location:
Section 9.2.5, "Bayes for the exponential family," pages 287-289

## See also

-No Additional Notes-