Learning Gaussian graphical models with fractional marginal pseudo-likelihood

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Janne Leppä-Aho, Johan Pensar, Teemu Roos, Jukka Corander
Publisher: Elsevier
Publication year: 2017
Journal: International Journal of Approximate Reasoning
Journal acronym: IJAR
Volume number: 83
Start page: 21
End page: 42


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.


Last updated on 2019-21-11 at 02:57