Controlled Sequential Monte Carlo

Heng, J; Bishop, AN; Deligiannidis, G; Doucet, A

Controlled Sequential Monte Carlo

Heng, J Bishop, AN Deligiannidis, G Doucet, A

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Publisher:: Institute of Mathematical Statistics
Publication Type:: Journal Article
Citation:: Annals of Statistics, 2020, 48, (5), pp. 2904-2929
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Heng, J
dc.contributor.author	Bishop, AN
dc.contributor.author	Deligiannidis, G
dc.contributor.author	Doucet, A
dc.date.accessioned	2022-08-25T20:12:29Z
dc.date.available	2022-08-25T20:12:29Z
dc.date.issued	2020
dc.identifier.citation	Annals of Statistics, 2020, 48, (5), pp. 2904-2929
dc.identifier.issn	0090-5364
dc.identifier.issn	2168-8966
dc.identifier.uri	http://hdl.handle.net/10453/160872
dc.description.abstract	Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques to approximate high-dimensional probability distributions and their normalizing constants. They have found numerous applications in statistics and related fields as they can be applied to perform state estimation for non-linear non-Gaussian state space models and Bayesian inference for complex static models. Like many Monte Carlo sampling schemes, they rely on proposal distributions which have a crucial impact on their performance. We introduce here a class of controlled sequential Monte Carlo algorithms, where the proposal distributions are determined by approximating the solution to an associated optimal control problem using an iterative scheme. We provide theoretical analysis of our proposed methodology and demonstrate significant gains over state-of-the-art methods at a fixed computational complexity on a variety of applications.
dc.language	en
dc.publisher	Institute of Mathematical Statistics
dc.relation.ispartof	Annals of Statistics
dc.relation.isbasedon	10.1214/19-AOS1914
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0102 Applied Mathematics, 0104 Statistics, 1403 Econometrics
dc.subject.classification	Statistics & Probability
dc.title	Controlled Sequential Monte Carlo
dc.type	Journal Article
utslib.citation.volume	48
utslib.for	0102 Applied Mathematics
utslib.for	0104 Statistics
utslib.for	1403 Econometrics
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/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-25T20:12:24Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	48
utslib.citation.issue	5

Abstract:

Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques to approximate high-dimensional probability distributions and their normalizing constants. They have found numerous applications in statistics and related fields as they can be applied to perform state estimation for non-linear non-Gaussian state space models and Bayesian inference for complex static models. Like many Monte Carlo sampling schemes, they rely on proposal distributions which have a crucial impact on their performance. We introduce here a class of controlled sequential Monte Carlo algorithms, where the proposal distributions are determined by approximating the solution to an associated optimal control problem using an iterative scheme. We provide theoretical analysis of our proposed methodology and demonstrate significant gains over state-of-the-art methods at a fixed computational complexity on a variety of applications.

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http://hdl.handle.net/10453/160872