Approximate Bayesian inference for large spatial datasets using predictive process models

Publication year: 2011 Source: Computational Statistics & Data Analysis, Available online 4 November 2011 Jo Eidsvik, Andrew O. Finley, Sudipto Banerjee, Havard Rue The challenges of estimating hierarchical spatial models to large datasets are addressed. With the increasing availability of geocoded scientific data, hierarchical models involving spatial processes have become a popular method for carrying out spatial inference. Such models are customarily estimated using Markov chain Monte Carlo algorithms that, while immensely flexible, can become prohibitively expensive.

Publication year: 2011 Source: Computational Statistics & Data Analysis, Available online 4 November 2011 Jo Eidsvik, Andrew O. Finley, Sudipto Banerjee, Havard Rue The challenges of estimating hierarchical spatial models to large datasets are addressed. With the increasing availability of geocoded scientific data, hierarchical models involving spatial processes have become a popular method for carrying out spatial inference. Such models are customarily estimated using Markov chain Monte Carlo algorithms that, while immensely flexible, can become prohibitively expensive.

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Approximate Bayesian inference for large spatial datasets using predictive process models