Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference



Wu, Jiangqi, Wen, Linjie, Green, Peter L, Li, Jinglai and Maskell, Simon ORCID: 0000-0003-1917-2913
(2022) Ensemble Kalman filter based sequential Monte Carlo sampler for sequential Bayesian inference. STATISTICS AND COMPUTING, 32 (1). 20-.

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Abstract

<jats:title>Abstract</jats:title><jats:p>Many real-world problems require one to estimate parameters of interest, in a Bayesian framework, from data that are collected sequentially in time. Conventional methods for sampling from posterior distributions, such as Markov chain Monte Carlo cannot efficiently address such problems as they do not take advantage of the data’s sequential structure. To this end, sequential methods which seek to update the posterior distribution whenever a new collection of data become available are often used to solve these types of problems. Two popular choices of sequential method are the ensemble Kalman filter (EnKF) and the sequential Monte Carlo sampler (SMCS). While EnKF only computes a Gaussian approximation of the posterior distribution, SMCS can draw samples directly from the posterior. Its performance, however, depends critically upon the kernels that are used. In this work, we present a method that constructs the kernels of SMCS using an EnKF formulation, and we demonstrate the performance of the method with numerical examples.</jats:p>

Item Type: Article
Uncontrolled Keywords: Ensemble Kalman filter, Parameter estimation, Sequential Bayesian inference, Sequential Monte Carlo sampler
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Faculty of Science and Engineering > School of Engineering
Depositing User: Symplectic Admin
Date Deposited: 14 Dec 2021 08:30
Last Modified: 15 Mar 2024 07:09
DOI: 10.1007/s11222-021-10075-x
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3145326

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