NDLI: Bayesian filtering with intractable likelihood using sequential MCMC

Content Provider	IEEE Xplore Digital Library
Author	Septier, F. Peters, G.W. Nevat, I.
Copyright Year	2013
Description	Author affiliation: Wireless & Networking Tech. Lab., CSIRO, Sydney, NSW, Australia (Nevat, I.) \|\| Telecom Lille 1, LAGIS, Inst. Mines-Telecom, Lille, France (Septier, F.) \|\| Dept. of Stat. Sci., Univ. Coll. London, London, UK (Peters, G.W.)
Abstract	We develop a sequential estimation methodology for a class of nonlinear, non-Gaussian state space models in which the observation process is intractable to express in closed form, but trivial to simulate. In addition we consider models in which the latent state vector and the observation vector are very high dimensional. To overcome these two difficulties we propose the class of Sequential Markov chain Monte Carlo (SMCMC) algorithms in which we incorporate a component of Approximate Bayesian Computation (ABC). In doing so we tackle both the curse of dimensionality via the SMCMC and the intractability of the likelihood via the ABC component. We demonstrate how the proposed algorithm outperforms alternative approaches in two challenging state space model examples.
Starting Page	6313
Ending Page	6317
File Size	168136
Page Count	5
File Format	PDF
ISBN	9781479903566
ISSN	15206149
DOI	10.1109/ICASSP.2013.6638880
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-05-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Approximation methods Bayes methods Monte Carlo methods Vectors Approximation algorithms Hidden Markov models Filtering approximate Bayesian computation Bayesian filtering intractable likelihood MCMC
Content Type	Text
Resource Type	Article

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