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Sequential Monte Carlo with transformations

Abstract:

This paper examines methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. For this, we use sequential Monte Carlo samplers, introducing the innovation of using deterministic transformations to move particles effectively between target distributions with different dimensions. This approach, combined with adaptive methods, yields an extremely flexible and general algorithm for Bayesian model comparison that is suitable for use ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s11222-019-09903-y

Authors


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Role:
Author
ORCID:
0000-0002-0822-5648
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Role:
Author
ORCID:
0000-0003-4057-3906
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Institution:
University of Oxford
Division:
ContEd
Sub department:
BX POPULATION HEALTH; EQ CONTINUING EDUCATION - EQ CENTRAL
Oxford college:
St Cross College
Role:
Author
ORCID:
0000-0002-0940-3311
Publisher:
Springer
Journal:
Statistics and Computing More from this journal
Volume:
30
Issue:
3
Pages:
663-676
Publication date:
2019-11-17
Acceptance date:
2019-09-03
DOI:
EISSN:
1573-1375
ISSN:
0960-3174
Pmid:
32116416
Language:
English
Keywords:
Pubs id:
1076790
Local pid:
pubs:1076790
Deposit date:
2021-01-19

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