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

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), pp. 1462-1486. (doi:10.1162/NECO_a_00281). (PMID:22364499).

Record type: Article


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


Local EPrints ID: 337632
PURE UUID: 0419ebee-d917-4156-8290-858844717389

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Date deposited: 01 May 2012 08:02
Last modified: 18 Jul 2017 06:02

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Author: Ke Yuan
Author: Mark Girolami

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