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hal-00652092, version 1

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

Fabien Campillo (, http://www-sop.inria.fr/mere/personnel/campillo/) 1, Rivo Rakotozafy () 2

7th African Conference on Research in Computer Science (2004)

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.

  • 1:  ASPI (INRIA - IRISA)
  • CNRS : UMR6074 – INRIA – Université de Rennes 1
  • 2:  Université de Fianarantsoa
  • Université de Fianarantsoa
  • Domain : Mathematics/Probability
 
  • hal-00652092, version 1
  • http://hal.inria.fr/hal-00652092
  • oai:hal.inria.fr:hal-00652092
  • From: Fabien Campillo <>
  • Submitted on: Wednesday, 14 December 2011 18:51:50
  • Updated on: Thursday, 23 February 2012 16:44:00