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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
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 > 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: |
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| 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 | ||||
| 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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