Model fitting and Bayesian inference via power expectation propagation

Degani, E; Maestrini, L; Bernardi, M

Model fitting and Bayesian inference via power expectation propagation

Degani, E Maestrini, L

Bernardi, M

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Publication Type:: Conference Proceeding
Citation:: 2021
Issue Date:: 2021-11-01

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Field	Value	Language
dc.contributor.author	Degani, E
dc.contributor.author	Maestrini, L https://orcid.org/0000-0003-4645-4967
dc.contributor.author	Bernardi, M
dc.contributor.editor	Perna, C
dc.contributor.editor	Salvati, N
dc.contributor.editor	Schirripa Spagnolo, F
dc.date	2021-06-21
dc.date.accessioned	2022-02-10T21:58:13Z
dc.date.available	2022-02-10T21:58:13Z
dc.date.issued	2021-11-01
dc.identifier.citation	2021
dc.identifier.isbn	9788891927361
dc.identifier.uri	http://hdl.handle.net/10453/154388
dc.description.abstract	We study a message passing approach to power expectation propagation for Bayesian model fitting and inference. Power expectation propagation is a class of variational approximations based on the notion of α-divergence that extends two notable approximations, namely mean field variational Bayes and expectation propagation. An illustration on a simple model allows to grasp benefits and complexities of this methodology and sets the basis for applications on more complex models.
dc.language	en
dc.relation.ispartofseries	Book of Short Papers SIS 2021
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Model fitting and Bayesian inference via power expectation propagation
dc.type	Conference Proceeding
utslib.location.activity	Italy, online
pubs.organisational-group	/University of Technology Sydney
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
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-02-10T21:58:11Z
pubs.finish-date	2021-06-25
pubs.publication-status	Published online
pubs.start-date	2021-06-21

Abstract:

We study a message passing approach to power expectation propagation for Bayesian model fitting and inference. Power expectation propagation is a class of variational approximations based on the notion of α-divergence that extends two notable approximations, namely mean field variational Bayes and expectation propagation. An illustration on a simple model allows to grasp benefits and complexities of this methodology and sets the basis for applications on more complex models.

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http://hdl.handle.net/10453/154388