Marginal Likelihood Integrals for Mixtures of Independence Models

Evaluation of marginal likelihood integrals is central to Bayesian statistics. It is generally assumed that these integrals cannot be evaluated exactly, except in trivial case, and many techniques (e.g. MCMC) have been developed to obtain asymptotics and approximations. This lecture argues that exact integration is more feasible than is widely believed. We present an exact algebraic method for the computation of marginal likelihood integrals for a class of mixture models for discrete data.

This is joint work in progress with Shaowei Lin and Zhiqiang Xu.