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Particle filtering for Bayesian parameter estimation in a high dimensional state space model

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Title: Particle filtering for Bayesian parameter estimation in a high dimensional state space model
Authors: Miguez, J
Crisan, D
Marino, IP
Item Type: Conference Paper
Abstract: Researchers in some of the most active fields of science, including, e.g., geophysics or systems biology, have to deal with very-large-scale stochastic dynamic models of real world phenomena for which conventional prediction and estimation methods are not well suited. In this paper, we investigate the application of a novel nested particle filtering scheme for joint Bayesian parameter estimation and tracking of the dynamic variables in a high dimensional state space model-namely a stochastic version of the two-scale Lorenz 96 chaotic system, commonly used as a benchmark model in meteorology and climate science. We provide theoretical guarantees on the algorithm performance, including uniform convergence rates for the approximation of posterior probability density functions of the fixed model parameters.
Issue Date: 28-Dec-2015
Date of Acceptance: 31-Aug-2015
URI: http://hdl.handle.net/10044/1/53336
DOI: https://dx.doi.org/10.1109/EUSIPCO.2015.7362582
ISBN: 9780992862633
Publisher: IEEE
Start Page: 1241
End Page: 1245
Journal / Book Title: 2015 23rd European Signal Processing Conference, EUSIPCO 2015
Copyright Statement: © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/H000550/1
Conference Name: EUSIPCO 2015
Publication Status: Published
Start Date: 2015-08-31
Finish Date: 2015-09-04
Conference Place: Nice France
Appears in Collections:Pure Mathematics
Mathematics