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.