Density estimation via Bayesian inference engines

Wand, MP; Yu, JCF

Density estimation via Bayesian inference engines

Wand, MP Yu, JCF

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: A St A: Advances in Statistical Analysis, 2022, 106, (2), pp. 199-216
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Wand, MP
dc.contributor.author	Yu, JCF
dc.date.accessioned	2023-02-22T04:08:20Z
dc.date.available	2023-02-22T04:08:20Z
dc.date.issued	2022-01-01
dc.identifier.citation	A St A: Advances in Statistical Analysis, 2022, 106, (2), pp. 199-216
dc.identifier.issn	0002-6018
dc.identifier.issn	1863-818X
dc.identifier.uri	http://hdl.handle.net/10453/166309
dc.description.abstract	We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as those based on no-U-turn sampling and expectation propagation. Extensive simulation studies demonstrate that the proposed density estimates have excellent comparative performance and scale well to very large sample sizes due to a binning strategy. Moreover, the approach is fully Bayesian and all estimates are accompanied by point-wise credible intervals. An accompanying package in the R language facilitates easy use of the new density estimates.
dc.language	English
dc.publisher	Springer
dc.relation	http://purl.org/au-research/grants/arc/DP140100441
dc.relation.ispartof	A St A: Advances in Statistical Analysis
dc.relation.isbasedon	10.1007/s10182-021-00422-8
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0104 Statistics
dc.subject.classification	Economics
dc.title	Density estimation via Bayesian inference engines
dc.type	Journal Article
utslib.citation.volume	106
utslib.for	0104 Statistics
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	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2023-02-22T04:08:19Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	106
utslib.citation.issue	2

Abstract:

We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as those based on no-U-turn sampling and expectation propagation. Extensive simulation studies demonstrate that the proposed density estimates have excellent comparative performance and scale well to very large sample sizes due to a binning strategy. Moreover, the approach is fully Bayesian and all estimates are accompanied by point-wise credible intervals. An accompanying package in the R language facilitates easy use of the new density estimates.

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http://hdl.handle.net/10453/166309