Sökning: onr:"swepub:oai:DiVA.org:liu-159811" >
Retracted article: ...
Retracted article: Smoothing with Couplings of Conditional Particle Filters
-
- Jacob, Pierre (författare)
- Harvard University, USA
-
- Lindsten, Fredrik (författare)
- Uppsala universitet, Avdelningen för systemteknik, Sweden
-
- Schön, Thomas B., Professor, 1977- (författare)
- Uppsala universitet, Avdelningen för systemteknik, Sweden
-
(creator_code:org_t)
- 2018
- 2018
- Engelska.
-
Ingår i: Journal of the American Statistical Association. - : Taylor & Francis. - 0162-1459 .- 1537-274X.
- Relaterad länk:
-
https://doi.org/10.1...
-
visa fler...
-
https://liu.diva-por... (primary) (Raw object)
-
https://www.tandfonl...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- In state space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and confidence intervals can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly-informative observations, and for a realistic Lotka-Volterra model with an intractable transition density.
Ämnesord
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas