{MCMC} for non linear/non {Gaussian} state-space models: Application to fishery stock assessment

Fabien Campillo 1 Rivo Rakotozafy 2
1 ASPI - Applications of interacting particle systems to statistics
UR1 - Université de Rennes 1, Inria Rennes – Bretagne Atlantique , CNRS - Centre National de la Recherche Scientifique : UMR6074
Abstract : We consider a Monte Carlo Markov chain (MCMC) algorithm for fisheries stock assess- ment. The biomass of this stock at a given year could be modeled as a nonlinear function of the biomass and catch for the two previous years, of different parameters (recruitment, growth rate, nat- ural mortality rate). Given a time series of annual catch and effort data, we would like to achieve the best fitting between the data and a class of non linear/non Gaussian state-space models.
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Communication dans un congrès
7th African Conference on Research in Computer Science, Nov 2004, Hammamet, Tunisia. 2004
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Fabien Campillo, Rivo Rakotozafy. {MCMC} for non linear/non {Gaussian} state-space models: Application to fishery stock assessment. 7th African Conference on Research in Computer Science, Nov 2004, Hammamet, Tunisia. 2004. 〈hal-00652092〉

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