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Bayesian inference for a stochastic logistic model with switching points
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Tang, Sanyi and Heron, Elizabeth A.. (2008) Bayesian inference for a stochastic logistic model with switching points. Ecological Modelling, Vol.219 (No.12). pp. 153169. ISSN 03043800
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Official URL: http://dx.doi.org/10.1016/j.ecolmodel.2008.08.007
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 EulerMaruyama 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:  03043800  
Official Date:  24 November 2008  
Dates: 


Volume:  Vol.219  
Number:  No.12  
Number of Pages:  17  
Page Range:  pp. 153169  
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)  
References:  Alvarez, L.H.R., Shepp, L.A., 1998. Optimal harvesting of 

URI:  http://wrap.warwick.ac.uk/id/eprint/28930 
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