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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.1-2).
pp. 153-169.
ISSN 0304-3800

**Full text not available from this repository.**

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 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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