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Unbiased smoothing using Particle Independent Metropolis-Hastings

Abstract:

We consider the approximation of expectations with respect to the distribution of a latent Markov process given noisy measurements. This is known as the smoothing problem and is often approached with particle and Markov chain Monte Carlo (MCMC) methods. These methods provide consistent but biased estimators when run for a finite time. We propose a simple way of coupling two MCMC chains built using Particle Independent Metropolis-Hastings (PIMH) to produce unbiased smoothing estimators. Unbias...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
Oxford college:
Hertford College
Role:
Author
ORCID:
0000-0002-7662-419X
Publisher:
MLR Press Publisher's website
Volume:
89
Pages:
2378-2387
Publication date:
2019-04-11
Acceptance date:
2018-12-22
Pubs id:
pubs:962940
URN:
uri:c7542255-6c32-463c-a255-2860bd5df9d8
UUID:
uuid:c7542255-6c32-463c-a255-2860bd5df9d8
Local pid:
pubs:962940

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