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Markov chain Monte Carlo methods for state-space models with point process observations

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Yuan, Ke, Girolami, Mark and Niranjan, Mahesan (2012) Markov chain Monte Carlo methods for state-space models with point process observations. Neural Computation, Volume 24 (Number 6). pp. 1462-1486. doi:10.1162/NECO_a_00281

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Official URL: http://dx.doi.org/10.1162/NECO_a_00281

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Abstract

This letter considers how a number of modern Markov chain Monte Carlo (MCMC) methods can be applied for parameter estimation and inference in state-space models with point process observations. We quantified the efficiencies of these MCMC methods on synthetic data, and our results suggest that the Reimannian manifold Hamiltonian Monte Carlo method offers the best performance. We further compared such a method with a previously tested variational Bayes method on two experimental data sets. Results indicate similar performance on the large data sets and superior performance on small ones. The work offers an extensive suite of MCMC algorithms evaluated on an important class of models for physiological signal analysis.

Item Type: Journal Item
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science, Engineering and Medicine > Science > Statistics
Library of Congress Subject Headings (LCSH): Neural circuitry, Markov processes, Monte Carlo method
Journal or Publication Title: Neural Computation
Publisher: MIT Press
ISSN: 0899-7667
Official Date: June 2012
Dates:
DateEvent
June 2012Published
Volume: Volume 24
Number: Number 6
Page Range: pp. 1462-1486
DOI: 10.1162/NECO_a_00281
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 28 December 2015
Date of first compliant Open Access: 28 December 2015
Funder: University of Southampton, Engineering and Physical Sciences Research Council (EPSRC), Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC)
Grant number: EP/E052029/2 (EPSRC), EP/E032745/2 (EPSRC), EP/F009429/2 (EPSRC)
Conference Paper Type: Paper

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