Advanced Bayesian Modeling and Computational Methods
We extend the model structures described in the previous chapter using Bayesian hierarchical models. Because we generally cannot write the posterior distributions that result from these more complicated models in closed form, we begin this chapter with a description of Markov chain Monte Carlo algorithms that can be used to generate samples from intractable posterior distributions. These samples provide the basis for subsequent model inference. We also discuss empirical Bayes’ methods. Finally, we describe techniques for assessing the sensitivity of model inferences to prior assumptions and a broadly applicable model diagnostic.
KeywordsPosterior Distribution Markov Chain Monte Carlo Prior Distribution Success Probability Advance Modeling
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