Fast Bayesian intensity estimation for the permanental process

Walder, CJ; Bishop, AN

Fast Bayesian intensity estimation for the permanental process

Walder, CJ Bishop, AN

Permalink

Publication Type:: Conference Proceeding
Citation:: 34th International Conference on Machine Learning, ICML 2017, 2017, 7 pp. 5459 - 5471
Issue Date:: 2017-01-01

Closed Access

	Filename	Description	Size
	walder17a.pdf	Published version	2.69 MB	Adobe PDF	View/Open
	1701.03535v3.pdf	Accepted Manuscript version	4.89 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Walder, CJ	en_US
dc.contributor.author	Bishop, AN https://orcid.org/0000-0001-8581-8562	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	34th International Conference on Machine Learning, ICML 2017, 2017, 7 pp. 5459 - 5471	en_US
dc.identifier.isbn	9781510855144	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127627
dc.description.abstract	Copyright © 2017 by the authors. The Cox process is a stochastic process which generalises the Poisson process by letting the underlying intensity function itself be a stochastic process. In this paper we present a fast Bayesian inference scheme for the permanental process, a Cox process under which the square root of the intensity is a Gaussian process. In particular we exploit connections with reproducing kernel Hilbert spaces, to derive efficient approximate Bayesian inference algorithms based on the Laplace approximation to the predictive distribu-tion and marginal likelihood. We obtain a simple algorithm which we apply to toy and real-world problems, obtaining orders of magnitude speed improvements over previous work.	en_US
dc.relation.ispartof	34th International Conference on Machine Learning, ICML 2017	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Fast Bayesian intensity estimation for the permanental process	en_US
dc.type	Conference Proceeding
utslib.citation.volume	7	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

Copyright © 2017 by the authors. The Cox process is a stochastic process which generalises the Poisson process by letting the underlying intensity function itself be a stochastic process. In this paper we present a fast Bayesian inference scheme for the permanental process, a Cox process under which the square root of the intensity is a Gaussian process. In particular we exploit connections with reproducing kernel Hilbert spaces, to derive efficient approximate Bayesian inference algorithms based on the Laplace approximation to the predictive distribu-tion and marginal likelihood. We obtain a simple algorithm which we apply to toy and real-world problems, obtaining orders of magnitude speed improvements over previous work.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/127627