Overview: MCMC Procedure. The MCMC procedure is a general purpose Markov chain Monte Carlo (MCMC) simulation procedure that is designed to fit Bayesian models. Bayesian statistics is different from traditional statistical methods such as frequentist or classical methods. For a short introduction to Bayesian analysis and related basic concepts, see Chapter 7, Introduction to Bayesian Analysis Procedures. Also see the section A Bayesian Reading List for a guide to Bayesian textbooks of varying degrees of difficulty. In essence, Bayesian statistics treats parameters as unknown random variables, and it makes inferences based on the posterior distributions of the parameters. There are several advantages associated with this approach to statistical inference. Some of the advantages include its ability to use prior information and to directly answer specific scientific questions that can be easily understood. For further discussions of the relative advantages and disadvantages of Bayesian analysis, see the section Bayesian Analysis: Advantages and Disadvantages. ...

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  1. Brown, Helen; Prescott, Robin: Applied mixed models in medicine (2015)