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Exact Inference on Conditional Linear Γ-Gaussian Bayesian Networks.

Conference paper
Authors Ivar Simonsson
Petter Mostad
Published in Proceedings of the Eighth International Conference on Probabilistic Graphical Models. Volume 52 of the JMLR Workshop and Conference Proceedings: PGM 2016, Lugano, 6–9 September 2016
ISSN 1938-7228
Publication year 2016
Published at Department of Mathematical Sciences, Mathematical Statistics
Language en
Keywords Hybrid Bayesian networks; inference in hybrid Bayesian networks; variable elimina- tion algorithm
Subject categories Mathematics


Exact inference for Bayesian Networks is only possible for quite limited classes of networks. Ex- amples of such classes are discrete networks, conditional linear Gaussian networks, networks using mixtures of truncated exponentials, and networks with densities expressed as truncated polynomi- als. This paper defines another class with exact inference, based on the normal inverse gamma conjugacy. We describe the theory of this class as well as exemplify our implemented inference algorithm in a practical example. Although generally small and simple, we believe these kinds of networks are potentially quite useful, on their own or in combination with other algorithms and methods for Bayesian Network inference.

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