The Inverse G-Wishart Distribution and Variational Message Passing

Maestrini, L; Wand, MP

The Inverse G-Wishart Distribution and Variational Message Passing

Maestrini, L

Wand, MP

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Field	Value	Language
dc.contributor.author	Maestrini, L https://orcid.org/0000-0003-4645-4967
dc.contributor.author	Wand, MP
dc.date.accessioned	2020-12-07T02:50:12Z
dc.date.available	2020-12-07T02:50:12Z
dc.identifier.uri	http://hdl.handle.net/10453/144508
dc.description.abstract	Message passing on a factor graph is a powerful paradigm for the coding of approximate inference algorithms for arbitrarily graphical large models. The notion of a factor graph fragment allows for compartmentalization of algebra and computer code. We show that the Inverse G-Wishart family of distributions enables fundamental variational message passing factor graph fragments to be expressed elegantly and succinctly. Such fragments arise in models for which approximate inference concerning covariance matrix or variance parameters is made, and are ubiquitous in contemporary statistics and machine learning.
dc.language	en
dc.rights	info:eu-repo/semantics/openAccess
dc.title	The Inverse G-Wishart Distribution and Variational Message Passing
dc.type	Journal Article
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2020-12-07T02:50:05Z

Abstract:

Message passing on a factor graph is a powerful paradigm for the coding of approximate inference algorithms for arbitrarily graphical large models. The notion of a factor graph fragment allows for compartmentalization of algebra and computer code. We show that the Inverse G-Wishart family of distributions enables fundamental variational message passing factor graph fragments to be expressed elegantly and succinctly. Such fragments arise in models for which approximate inference concerning covariance matrix or variance parameters is made, and are ubiquitous in contemporary statistics and machine learning.

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http://hdl.handle.net/10453/144508