Journal article icon

Journal article

Controlled sequential Monte Carlo

Abstract:

Sequential Monte Carlo methods, also known as particle methods, are a popular set of techniques for approximating high-dimensional probability distributions and their normalizing constants. These methods have found numerous applications in statistics and related fields; for example, for inference in nonlinear non-Gaussian state space models, and in complex static models. Like many Monte Carlo sampling schemes, they rely on proposal distributions which crucially impact their performance. We in...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Files:
Publisher copy:
10.1214/19-AOS1914

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
Brasenose College
Role:
Author
ORCID:
0000-0002-0821-4607
Publisher:
Institute of Mathematical Statistics Publisher's website
Journal:
Annals of Statistics Journal website
Volume:
48
Issue:
5
Pages:
2904-2929
Publication date:
2020-09-19
Acceptance date:
2019-09-21
DOI:
ISSN:
0090-5364
Source identifiers:
1056043
Language:
English
Keywords:
Pubs id:
pubs:1056043
UUID:
uuid:4f4efb19-4344-4168-b762-3808d6fadfc2
Local pid:
pubs:1056043
Deposit date:
2019-09-27

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP