Tang, Sanyi and Heron, Elizabeth A.. (2008) Bayesian inference for a stochastic logistic model with switching points. Ecological Modelling, Vol.219 (No.1-2). pp. 153-169. ISSN 0304-3800

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Abstract

In this paper we use Markov chain Monte Carlo (MCMC) techniques to carry out Bayesian inference for piecewise stochastic logistic growth models using discretely observed data sets, which allows us to fit models for time series data, including data on fish productions and yields, with structural changes. The estimation framework involves the introduction of latent data points between each pair of observations, and the use of MCMC techniques, based on the Gibbs sampling algorithm, in conjunction with the Euler-Maruyama discretization. scheme. These methods are used to sample from the posterior distribution using exact bridges, allowing estimation of the model parameters including switching point(s). We apply our methods to examples involving both simulated data and real data for fisheries resources management. (C) 2008 Elsevier BY. All rights reserved.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics Q Science > QH Natural history > QH301 Biology
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Bayesian statistical decision theory, Markov processes, Monte Carlo method, Stochastic models, Fishery resources -- Management -- Mathematical models
Journal or Publication Title:	Ecological Modelling
Publisher:	Elsevier BV
ISSN:	0304-3800
Date:	24 November 2008
Volume:	Vol.219
Number:	No.1-2
Number of Pages:	17
Page Range:	pp. 153-169
Identification Number:	10.1016/j.ecolmodel.2008.08.007
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Guo jia zi ran ke xue ji jin wei yuan hui (China) [National Natural Science Foundation of China] (NSFC)
Grant number:	10871122 (NSFC)
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URI:	http://wrap.warwick.ac.uk/id/eprint/28930

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