ABSTRACT
Bayesian methods are rapidly becoming more popular with the greatly increased power
of Markov chain Monte Carlo (MCMC or MC2) methods for inference in complex
models. Such features as missing or incomplete data, latent variables, and nonconjugate
prior distributions can be handled in a unified way. Bayesian methods solve inferential
problems that are difficult to deal with in frequentist (repeated-sampling) theory:
• The inadequacy of asymptotic theory in small samples and the difficulties of
second-order asymptotics.
