Bayesian mixture models in a longitudinal setting for analysing sheep CAT scan images

  • Authors:

  • C. L. Alston;K. L. Mengersen;C. P. Robert;J. M. Thompson;P. J. Littlefield;D. Perry;A. J. Ball

  • Affiliations:

  • School of Mathematical and Physical Sciences, University of Newcastle, Callaghan, 2308, NSW, Australia;School of Mathematical Sciences, Queensland University of Technology, Brisbane, 4001, QLD, Australia;CEREMADE, Université Paris Dauphine, 75775 Paris cedex 16, France and CREST-INSEE, France;Co-operative Research Centre for the Cattle and Beef Industries, University of New England, Armidale, NSW, 2351, Australia;Co-operative Research Centre for the Cattle and Beef Industries, University of New England, Armidale, NSW, 2351, Australia;Co-operative Research Centre for the Cattle and Beef Industries, University of New England, Armidale, NSW, 2351, Australia;Meat and Livestock Australia, University of New England, Armidale, NSW, 2351, Australia

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2007

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Abstract

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.