Hierarchical Bayesian Transition Models for the Analysis of Binary Longitudinal Data

Abstract

The paper proposes a general hierarchical Bayesian methodology for the analysis of binary longitudinal data. The Bayesian method is implemented via Markov Chain Monte Carlo (MCMC) integration technique and is applied to a real dataset which was collected to study stereotyped behavior in deer mice.

Keywords

Binary Longitudinal data Thansition models Markov Chain Monte Carlo Deer mice Standard caging Enriched caging.

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