The University of Southampton
University of Southampton Institutional Repository

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

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.

Other NECO_a_00281 - Other
Download (1MB)

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

Catalogue record

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

Export record

Altmetrics

Contributors

Author: Ke Yuan
Author: Mark Girolami

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×