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Pseudo-likelihood Inference for Gaussian Markov Random Fields

Pseudo-likelihood Inference for Gaussian Markov Random Fields Gaussian Markov random fields (GMRFs) are an important example of MRFs with many applications, particularly because GMRFs are known to provide effective approximations to any MRF. Despite their relative computational simplicity, inference in GMRFs using maximum likelihood (ML) is not always feasible. Therefore, this paper compares the inference quality using the pseudolikelihood, a well-known computational shortcut to full ML, and in addition the generalized lambda distribution is simulated to investigate robustness to departure from the Gaussian distribution. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistics Research Letters Science and Engineering Publishing Company

Pseudo-likelihood Inference for Gaussian Markov Random Fields

Tom Burr; Alexei Skurikhin
Statistics Research Letters , Volume 2 (3) – Aug 1, 2013
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Publisher
Science and Engineering Publishing Company
Copyright
Science and Engineering Publishing Company
ISSN
2325-7040
eISSN
2325-7059

Abstract

Gaussian Markov random fields (GMRFs) are an important example of MRFs with many applications, particularly because GMRFs are known to provide effective approximations to any MRF. Despite their relative computational simplicity, inference in GMRFs using maximum likelihood (ML) is not always feasible. Therefore, this paper compares the inference quality using the pseudolikelihood, a well-known computational shortcut to full ML, and in addition the generalized lambda distribution is simulated to investigate robustness to departure from the Gaussian distribution.

Journal

Statistics Research Letters – Science and Engineering Publishing Company

Published: Aug 1, 2013

Keywords: Gaussian Markov Random Fields; Pseudolikelihood; Robustness

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