Learning Gaussian graphical models with fractional marginal pseudo-likelihood

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http://hdl.handle.net/10138/297770

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Leppä-Aho , J , Pensar , J , Roos , T T & Corander , J 2017 , ' Learning Gaussian graphical models with fractional marginal pseudo-likelihood ' , International Journal of Approximate Reasoning , vol. 83 , pp. 21-42 . https://doi.org/10.1016/j.ijar.2017.01.001

Title: Learning Gaussian graphical models with fractional marginal pseudo-likelihood
Author: Leppä-Aho, Janne; Pensar, Johan; Roos, Teemu Teppo; Corander, Jukka
Contributor: University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Department of Computer Science
University of Helsinki, Department of Mathematics and Statistics
Date: 2017-01-11
Language: eng
Number of pages: 22
Belongs to series: International Journal of Approximate Reasoning
ISSN: 0888-613X
URI: http://hdl.handle.net/10138/297770
Abstract: We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model. Using pseudo-likelihood, we derive an analytical expression to approximate the marginal likelihood for an arbitrary graph structure without invoking any assumptions about decomposability. The majority of the existing methods for learning Gaussian graphical models are either restricted to decomposable graphs or require specification of a tuning parameter that may have a substantial impact on learned structures. By combining a simple sparsity inducing prior for the graph structures with a default reference prior for the model parameters, we obtain a fast and easily applicable scoring function that works well for even high-dimensional data. We demonstrate the favourable performance of our approach by large-scale comparisons against the leading methods for learning non-decomposable Gaussian graphical models. A theoretical justification for our method is provided by showing that it yields a consistent estimator of the graph structure. (C) 2017 Elsevier Inc. All rights reserved.
Subject: 113 Computer and information sciences
112 Statistics and probability
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