The first part of the book lays out foundational material that will be leveraged in the second part of the book. In Chapter 1, we set out background material on assessment, psychometrics, probability, and model-based reasoning, setting the stage for developments to come. Chapters 2 and 3 provide an introduction to Bayesian inference and treat Bernoulli and binomial models. Whereas Chapter 2 focuses on the machinery of Bayesian inference, Chapter 3 delves into more conceptual issues. Chapter 4 reviews models for normal distributions. Chapter 5 discusses strategies for estimating posterior distributions, with an emphasis on Markov chain Monte Carlo methods used in the remainder of the book. Chapter 6 treats basic regression ...
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