- Abstract:
-
Many Markov chain Monte Carlo techniques currently available rely on discrete-time reversible Markov processes whose transition kernels are variations of the Metropolis–Hastings algorithm. We explore and generalize an alternative scheme recently introduced in the physics literature where the target distribution is explored using a continuous-time non-reversible piecewise-deterministic Markov process. In the Metropolis–Hastings algorithm, a trial move to a region of lower target density, equiv...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Accepted manuscript
- Files:
-
-
(pdf, 1.9mb)
-
- Publisher copy:
- 10.1080/01621459.2017.1294075
-
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- Funding agency for:
- Doucet, A
- Grant:
- Discovery Grant
- Publisher:
- Taylor & Francis Publisher's website
- Journal:
- Journal of the American Statistical Association Journal website
- Volume:
- 113
- Issue:
- 522
- Pages:
- 855-867
- Publication date:
- 2017-02-28
- Acceptance date:
- 2017-01-06
- DOI:
- EISSN:
-
1537-274X
- ISSN:
-
0162-1459
- Pubs id:
-
pubs:679244
- URN:
-
uri:301047da-e7f2-4cd7-9026-fdd5c3046903
- UUID:
-
uuid:301047da-e7f2-4cd7-9026-fdd5c3046903
- Local pid:
- pubs:679244
- Copyright holder:
- American Statistical Association
- Copyright date:
- 2017
- Notes:
- © 2017 American Statistical Association. This is the accepted manuscript version of the article. The final version is available online from Taylor and Francis at: https://doi.org/10.1080/01621459.2017.1294075
Journal article
The bouncy particle sampler: A non-reversible rejection free Markov chain Monte Carlo method
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+ Engineering and Physical Sciences Research Council
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+ Canadian National Science and Engineering Research Council
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