Conference item
Unbiased smoothing using Particle Independent Metropolis-Hastings
- Abstract:
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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
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(Version of record, pdf, 365.5KB)
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Funding
Bibliographic Details
- 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:
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962940
Item Description
- Pubs id:
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pubs:962940
- UUID:
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uuid:c7542255-6c32-463c-a255-2860bd5df9d8
- Local pid:
- pubs:962940
- Deposit date:
- 2019-01-16
Terms of use
- Copyright holder:
- Middleton et al
- Copyright date:
- 2019
- Notes:
- This paper has been made available under a Creative Commons Attribution 4.0 International License.
- Licence:
- CC Attribution (CC BY)
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