Slice sampling for simulation based fitting of spatial data models

An auxiliary variable method based on a slice sampler is shown to provide an attractive simulation-based model fitting strategy for fitting Bayesian models under proper priors. Though broadly applicable, we illustrate in the context of fitting spatial models for geo-referenced or point source data. Spatial modeling within a Bayesian framework offers inferential advantages and the slice sampler provides an algorithm which is essentially "off the shelf". Further potential advantages over importance sampling approaches and Metropolis approaches are noted and illustrative examples are supplied. © 2005 Springer Science + Business Media, Inc.

Duke Scholars

Author Alan E. Gelfand Statistical Science