Approximate inference for disease mapping with sparse Gaussian processes.
Stat Med
; 29(15): 1580-607, 2010 Jul 10.
Article
em En
| MEDLINE
| ID: mdl-20552572
ABSTRACT
Gaussian process (GP) models are widely used in disease mapping as they provide a natural framework for modeling spatial correlations. Their challenges, however, lie in computational burden and memory requirements. In disease mapping models, the other difficulty is inference, which is analytically intractable due to the non-Gaussian observation model. In this paper, we address both these challenges. We show how to efficiently build fully and partially independent conditional (FIC/PIC) sparse approximations for the GP in two-dimensional surface, and how to conduct approximate inference using expectation propagation (EP) algorithm and Laplace approximation (LA). We also propose to combine FIC with a compactly supported covariance function to construct a computationally efficient additive model that can model long and short length-scale spatial correlations simultaneously. The benefit of these approximations is computational. The sparse GPs speed up the computations and reduce the memory requirements. The posterior inference via EP and Laplace approximation is much faster and is practically as accurate as via Markov chain Monte Carlo.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Estudos Epidemiológicos
/
Doença
/
Modelos Estatísticos
Tipo de estudo:
Etiology_studies
/
Health_economic_evaluation
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Observational_studies
/
Risk_factors_studies
Limite:
Humans
País/Região como assunto:
Europa
Idioma:
En
Ano de publicação:
2010
Tipo de documento:
Article