کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4377810 1617526 2008 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Bayesian inference for a stochastic logistic model with switching points
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک بوم شناسی، تکامل، رفتار و سامانه شناسی
پیش نمایش صفحه اول مقاله
Bayesian inference for a stochastic logistic model with switching points
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ecological Modelling - Volume 219, Issues 1–2, 24 November 2008, Pages 153-169
نویسندگان
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