The goal of this book is to provide a unified introduction to a variety of computational algorithms that can be used as part of a Bayesian (posterior) analysis or as part of a likelihood analysis. These algorithms are tools and may be categorized using several taxonomies. The reader may find it useful to review these taxonomies as an aid to understanding how these tools complement, contrast and extend each other.
KeywordsGibbs Sampler Posterior Density General Social Survey Metropolis Algorithm Data Augmentation
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