Linear Models for Repeated Measures and Bayesian Inference

This chapter presents some introductory material, namely, some notation for the linear model, some preliminary information about modeling the mean profile of individuals over time and modeling the structure of the covariance matrix, reviewing some historical ways to analyze repeated measures, and last, a detailed presentation of the fundamentals of Bayesian inference. With regard to Bayesian inference, prior and posterior information is discussed as are Markov chain Monte Carlo (MCMC) techniques for the simulation of the posterior distribution of the model parameters, the parameters that model the mean profile and covariance structure of repeated measures.