Journal article
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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Authors
Funding
Wellcome Trust
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Bibliographic Details
- Publisher:
- Springer Publisher's website
- Journal:
- Statistics and Computing Journal website
- 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
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1076790
- Local pid:
- pubs:1076790
- Deposit date:
- 2021-01-19
Terms of use
- Copyright holder:
- Everitt et al.
- Copyright date:
- 2020
- Rights statement:
- © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecomm ons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Licence:
- CC Attribution (CC BY)
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