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Ensemble MCMC : accelerating pseudo-marginal MCMC for state space models using the ensemble Kalman filter
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Drovandi, Christopher, Everitt, Richard G., Golightly, Andrew and Prangle, Dennis (2021) Ensemble MCMC : accelerating pseudo-marginal MCMC for state space models using the ensemble Kalman filter. Bayesian Analysis . pp. 1-38. doi:10.1214/20-BA1251 ISSN 1931-6690.
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WRAP-ensemble-MCMC-accelerating-pseudo-marginal-MCMC-state-space-models-using-ensemble-Kalman-filter-Everitt-2020.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (1153Kb) |
Official URL: https://doi.org/10.1214/20-BA1251
Abstract
Particle Markov chain Monte Carlo (pMCMC) is now a popular method for performing Bayesian statistical inference on challenging state space models (SSMs) with unknown static parameters. It uses a particle lter (PF) at each iteration of an MCMC algorithm to unbiasedly estimate the likelihood for a given static parameter value. However, pMCMC can be computationally intensive when a large number of particles in the PF is required, such as when the data are highly informative, the model is misspeci ed and/or the time series is long. In this paper we exploit the ensemble Kalman lter (EnKF) developed in the data assimilation literature to speed up pMCMC. We replace the unbiased PF likelihood with the biased EnKF likelihood estimate within MCMC to sample over the space of the static parameter. On a wide class of di erent non-linear SSM models, we demonstrate that our extended ensemble MCMC (eMCMC) methods can signi cantly reduce the computational cost whilst maintaining reasonable accuracy. We also propose several extensions of the vanilla eMCMC algorithm to further improve computational e ciency. Computer code to implement our methods on all the examples can be downloaded from https://github.com/cdrovandi/Ensemble-MCMC.
Item Type: | Journal Article | ||||||||||||
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Subjects: | Q Science > QA Mathematics | ||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||||||||||
Library of Congress Subject Headings (LCSH): | Stochastic processes, Markov processes, Monte Carlo method, Kalman filtering, Bayesian statistical decision theory | ||||||||||||
Journal or Publication Title: | Bayesian Analysis | ||||||||||||
Publisher: | International Society for Bayesian Analysis | ||||||||||||
ISSN: | 1931-6690 | ||||||||||||
Official Date: | 2021 | ||||||||||||
Dates: |
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Page Range: | pp. 1-38 | ||||||||||||
DOI: | 10.1214/20-BA1251 | ||||||||||||
Status: | Peer Reviewed | ||||||||||||
Publication Status: | Published | ||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||||||
Date of first compliant deposit: | 18 November 2020 | ||||||||||||
Date of first compliant Open Access: | 18 December 2020 | ||||||||||||
RIOXX Funder/Project Grant: |
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