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

Markov chain Monte Carlo methods for state-space models with point process observations
Markov chain Monte Carlo methods for state-space models with point process observations
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.
1462-1486
Yuan, Ke
ae93001b-9371-4d70-88a5-ea53b2c429a9
Girolami, Mark
4feb7248-7beb-4edc-8509-139b4049d23b
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Yuan, Ke
ae93001b-9371-4d70-88a5-ea53b2c429a9
Girolami, Mark
4feb7248-7beb-4edc-8509-139b4049d23b
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f

Yuan, Ke, Girolami, Mark and Niranjan, Mahesan (2012) Markov chain Monte Carlo methods for state-space models with point process observations. Neural Computation, 24 (6), 1462-1486. (doi:10.1162/NECO_a_00281). (PMID:22364499)

Record type: Article

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.

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NECO_a_00281 - Other
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More information

e-pub ahead of print date: 25 April 2012
Published date: June 2012
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 337632
URI: http://eprints.soton.ac.uk/id/eprint/337632
PURE UUID: 0419ebee-d917-4156-8290-858844717389
ORCID for Mahesan Niranjan: ORCID iD orcid.org/0000-0001-7021-140X

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Date deposited: 01 May 2012 08:02
Last modified: 15 Mar 2024 03:29

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Contributors

Author: Ke Yuan
Author: Mark Girolami
Author: Mahesan Niranjan ORCID iD

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