Journal article
Reparameterized and Marginalized Posterior and Predictive Sampling for Complex Bayesian Geostatistical Models
Journal of Computational and Graphical Statistics, Vol.18(2), pp.262-282
01/01/2009
DOI: 10.1198/jcgs.2009.08012
Abstract
This article proposes a four-pronged approach to efficient Bayesian estimation and prediction for complex Bayesian hierarchical Gaussian models for spatial and spatiotemporal data. The method involves reparameterizing the covariance structure of the model, reformulating the means structure, marginalizing the joint posterior distribution, and applying a simplex-based slice sampling algorithm. The approach permits fusion of point-source data and areal data measured at different resolutions and accommodates nonspatial correlation and variance heterogeneity as well as spatial and/or temporal correlation. The method produces Markov chain Monte Carlo samplers with low autocorrelation in the output, so that fewer iterations are needed for Bayesian inference than would be the case with other sampling algorithms. Supplemental materials are available online.
Details
- Title: Subtitle
- Reparameterized and Marginalized Posterior and Predictive Sampling for Complex Bayesian Geostatistical Models
- Creators
- Mary Kathryn CowlesJun YanBrian Smith
- Resource Type
- Journal article
- Publication Details
- Journal of Computational and Graphical Statistics, Vol.18(2), pp.262-282
- DOI
- 10.1198/jcgs.2009.08012
- ISSN
- 1061-8600
- eISSN
- 1537-2715
- Publisher
- Taylor & Francis
- Language
- English
- Date published
- 01/01/2009
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Holden Comprehensive Cancer Center
- Record Identifier
- 9983985929402771
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