Abstract
Abstract Hierarchical models are one of the central tools of Bayesian analysis. They offer many advantages, including the ability to borrow strength to estimate individual parameters and the ability to specify complex models that reflect engineering and physical realities. Markov chain Monte Carlo (MCMC) is a set of algorithms that allow Bayesian inference in a variety of models. We illustrate hierarchical models and MCMC in a Bayesian system reliability example.
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