Bayesian mixture models in a longitudinal setting for analysing sheep CAT scan images
CAT scanning is used in longitudinal animal science experiments to assess possible changes to carcase composition induced by treatment over given time periods. A hierarchical Bayesian mixture model can be used to analyse the CAT scan data in terms of the proportion of each tissue type present in a scan. In this paper we present an extension to the hierarchical Bayesian mixture model in which estimated parameters from neighbouring CAT scans can be incorporated into the current model. These models are demonstrated using two examples.
density estimation gibbs sampling markov chain monte carlo markov random field metropolishastings algorithm posterior simulation perturbation parameters