Approximate Bayesian inference for large spatial datasets using predictive process models

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 estimat...

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Vydané v:	Computational statistics & data analysis Ročník 56; číslo 6; s. 1362 - 1380
Hlavní autori:	Eidsvik, Jo, Finley, Andrew O., Banerjee, Sudipto, Rue, Håvard
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.06.2012 Elsevier
Edícia:	Computational Statistics & Data Analysis
Predmet:	Algorithms Approximate Bayesian inference Approximate Bayesian inference Computational statistics Gaussian processes Geostatistics Laplace approximation Predictive process model Computation Computational statistics Computer simulation Estimating Gaussian processes Geostatistics Inference Laplace approximation Mathematical analysis Mathematical models Monte Carlo methods Predictive process model Approximate Bayesian inference Gaussian processes Geostatistics Computational statistics Laplace approximation Predictive process model
ISSN:	0167-9473, 1872-7352
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Shrnutí:	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. In particular, fitting hierarchical spatial models often involves expensive decompositions of dense matrices whose computational complexity increases in cubic order with the number of spatial locations. Such matrix computations are required in each iteration of the Markov chain Monte Carlo algorithm, rendering them infeasible for large spatial datasets. The computational challenges in analyzing large spatial datasets are considered by merging two recent developments. First, the predictive process model is used as a reduced-rank spatial process, to diminish the dimensionality of the model. Then a computational framework is developed for estimating predictive process models using the integrated nested Laplace approximation. The settings where the first stage likelihood is Gaussian or non-Gaussian are discussed. Issues such as predictions and model comparisons are also discussed. Results are presented for synthetic data and several environmental datasets.
Bibliografia:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0167-9473 1872-7352
DOI:	10.1016/j.csda.2011.10.022