Skip to Main content Skip to Navigation
Conference papers

{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.
Document type :
Conference papers
Complete list of metadata

Cited literature [6 references]  Display  Hide  Download

https://hal.inria.fr/hal-00652092
Contributor : Fabien Campillo <>
Submitted on : Wednesday, December 14, 2011 - 6:51:50 PM
Last modification on : Wednesday, October 14, 2020 - 3:04:19 AM
Long-term archiving on: : Thursday, March 15, 2012 - 2:45:51 AM

File

campillo2004b.pdf
Explicit agreement for this submission

Identifiers

  • HAL Id : hal-00652092, version 1

Citation

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. ⟨hal-00652092⟩

Share

Metrics

Record views

292

Files downloads

347