Journal Article

Estimating parameters in stochastic compartmental models using Markov chain methods

GAVIN J. GIBSON and ERIC RENSHAW

in Mathematical Medicine and Biology: A Journal of the IMA

Published on behalf of Institute of Mathematics and its Applications

Volume 15, issue 1, pages 19-40
Published in print March 1998 | ISSN: 1477-8599
Published online March 1998 | e-ISSN: 1477-8602 | DOI: http://dx.doi.org/10.1093/imammb/15.1.19
Estimating parameters in stochastic compartmental models using Markov chain methods

More Like This

Show all results sharing these subjects:

  • Applied Mathematics
  • Biomathematics and Statistics

GO

Show Summary Details

Preview

Markov chain Monte Carlo methodology is presented for estimating parameters in stochastic compartmental models from incomplete observations of the corresponding Markov process. The methods, which are based on the Metropolis-Hastings algorithm, are developed in the context of epidemic models. Their use is illustrated for the particular case where only susceptible, infective, and removed states are represented using simulated realizations of the process. By comparing estimated likelihoods with theoretical forms, in cases where these can be derived, or with the known model parameters, we show that the methods can be used to provide meaningful estimates of parameters and parameter uncertainty. Potential applications of the techniques are also discussed.

Keywords: stochastic compartment models; parameter estimation; Markov chain Monte Carlo methods; hidden Markov models.

Journal Article.  0 words. 

Subjects: Applied Mathematics ; Biomathematics and Statistics

Full text: subscription required

How to subscribe Recommend to my Librarian

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.