This book provides a unified introduction to a variety of computational algorithms for likelihood and Bayesian inference. The third edition expands the discussion of many of the techniques discussed, includes additional examples, and adds exercise sets at the end of each chapter.
Martin A. Tanner
Bayesian inference Gibbs sampler Likelihood Resampling algorithms expectation–maximization algorithm observed data