Conference item icon

Conference item

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...

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

Actions


Access Document


Files:

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Statistics
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
Hertford College
Role:
Author
ORCID:
0000-0002-7662-419X
Publisher:
MLR Press Publisher's website
Journal:
22nd International Conference on Artificial Intelligence and Statistics AIStats 2019 Journal website
Volume:
89
Pages:
2378-2387
Host title:
Proceedings of Machine Learning Research: 22nd International Conference on Artificial Intelligence and Statistics
Publication date:
2019-04-11
Acceptance date:
2018-12-22
Source identifiers:
962940
Pubs id:
pubs:962940
UUID:
uuid:c7542255-6c32-463c-a255-2860bd5df9d8
Local pid:
pubs:962940
Deposit date:
2019-01-16

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