Bayesian filtering with intractable likelihood using sequential MCMC

François Septier, Gareth W. Peters, Ido Nevat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing
PublisherIEEE
Pages6313-6317
Number of pages5
ISBN (Print)9781479903566
DOIs
Publication statusPublished - 21 Oct 2013
Event38th IEEE International Conference on Acoustics, Speech and Signal Processing 2013 - Vancouver, BC, Canada
Duration: 26 May 201331 May 2013

Conference

Conference38th IEEE International Conference on Acoustics, Speech and Signal Processing 2013
Abbreviated titleICASSP 2013
CountryCanada
CityVancouver, BC
Period26/05/1331/05/13

Keywords

  • approximate Bayesian computation
  • Bayesian filtering
  • intractable likelihood
  • MCMC

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Septier, F., Peters, G. W., & Nevat, I. (2013). Bayesian filtering with intractable likelihood using sequential MCMC. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 6313-6317). IEEE. https://doi.org/10.1109/ICASSP.2013.6638880