Statistics Seminars: Hidden Markov models and disease mapping
12 March 2004 14:00 in CM221
"We present new methodology to extend Hidden Markov models to the spatial domain, and use this class of models to analyse spatial heterogeneity of count data on a rare phenomenon. This situation occurs commonly in many domains of application, particularly in disease mapping.
We assume that the counts follow a Poisson model at the lowest level of the hierarchy, and introduce a finite mixture model for the Poisson rates at the next level. The novelty lies in the model for allocation to the mixture components, which follows a spatially correlated process, the Potts model, and in treating the number of components of the spatial mixture as unknown. The model introduced can be viewed as a Bayesian semiparametric approach to specifying flexible spatial distributions in hierarchical models. "
Double seminar with Brian Ripley!
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