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Activity Number: 84
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #319609
Title: Approximate Marginal Posterior for Log Gaussian Cox Processes
Author(s): Shinichiro Shirota* and Alan E. Gelfand
Companies: Duke University and
Keywords: pseudo-marginal approach ; spatial Cox processes ; log Gaussian Cox processes

The log Gaussian Cox process is a flexible class of point processes for incorporating complex structures of space and time point patterns. As for Bayesian inference, for rich spatial point patterns, we need to sample high dimensional Gaussian latent variables and it would lead to heavy computational costs. Furthermore, hyperparameters for Gaussian latent variables have high correlation with Gaussian latent variables themselves, so standard MCMC sampling strategy is inefficient. In this paper, we propose an efficient and scalable computational strategy for spatial log Gaussian Cox processes. The key aspect of this approach is to calculate the approximate marginal posterior of parameters by utilizing pseudo-marginal approach.

Authors who are presenting talks have a * after their name.

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