Title	Shrinkage priors for nonparametric Bayesian prediction of nonhomogeneous Poisson processes
Creator	Komaki, Fumiyasu
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2019-04-09T10:44
Description	A class of improper priors for nonhomogeneous Poisson intensity functions is proposed. The priors in the class have shrinkage properties. The nonparametric Bayesian predictive densities based on the shrinkage priors have reasonable properties, although improper priors have not been widely used for nonparametric Bayesian inference. In particular, the nonparametric Bayesian predictive densities are admissible under the Kullback-Leibler loss.
Extent	24.0 minutes
Subject	Mathematics; Statistics
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: The University of Tokyo
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2020-09-11
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0394323
URI	http://hdl.handle.net/2429/75980
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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