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Bayesian Analysis of Spatial Point Patterns

dc.contributor.advisor Gelfand, Alan E
dc.contributor.author Leininger, Thomas Jeffrey
dc.date.accessioned 2014-05-14T19:18:33Z
dc.date.available 2014-05-14T19:18:33Z
dc.date.issued 2014
dc.identifier.uri https://hdl.handle.net/10161/8730
dc.description.abstract <p>We explore the posterior inference available for Bayesian spatial point process models. In the literature, discussion of such models is usually focused on model fitting and rejecting complete spatial randomness, with model diagnostics and posterior inference often left as an afterthought. Posterior predictive point patterns are shown to be useful in performing model diagnostics and model selection, as well as providing a wide array of posterior model summaries. We prescribe Bayesian residuals and methods for cross-validation and model selection for Poisson processes, log-Gaussian Cox processes, Gibbs processes, and cluster processes. These novel approaches are demonstrated using existing datasets and simulation studies.</p>
dc.subject Statistics
dc.subject cross-validation
dc.subject Gibbs process
dc.subject Log-Gaussian Cox process
dc.subject model selection
dc.subject point pattern residuals
dc.subject Poisson process
dc.title Bayesian Analysis of Spatial Point Patterns
dc.type Dissertation
dc.department Statistical Science


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