Efficient Posterior Inference and Prediction of Space-Tim Processes Using Dynamic Process Convolutions

Catherine A. Calder
Department of Statistics
The Ohio State University
1958 Neil Avenue
Columbus, OH 43221
Ph. 614-688-0004; Fax 614-292-2096
E-mail: calder@stat.ohio-state.edu

Bayesian dynamic process convolution models provide an appealing approach for modeling both univariate and multivariate spatial temporal data. Their structure can be exploited to signi cantly reduce the dimensionality of a complex spatial temporal process. This results in ecient Markov chain Monte Carlo
(MCMC) algorithms required for full Bayesian inference. In addition, the dynamic process convolution framework readily handles both missing data and misaligned multivariate space-time data without the need for imputation. We review the dynamic process convolution framework and discuss these and other
computational advantages of the approach. We present an application involving the modeling of air pollutants to demonstrate how this approach can be used to e ectively model a space-time process and provide predictions along with corresponding uncertainty statements.

Keywords: Bayesian, factor analysis, dimension reduction, air pollution

In: McRoberts, R. et al. (eds).  Proceedings of the joint meeting of The 6th International Symposium On Spatial Accuracy Assessment In Natural Resources and Environmental Sciences and The 15th Annual Conference of The International Environmetrics Society, June 28 – July 1 2004, Portland, Maine, USA.

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