{MCMC} for non linear/non {Gaussian} state-space models: Application to fishery stock assessment
Campillo, Fabien ; Rakotozafy, Rivo
HAL, hal-00652092 / Harvested from HAL
Text on HAL
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.
Publié le : 2004-11-22
Classification:  [MATH.MATH-PR]Mathematics [math]/Probability [math.PR] 
@article{hal-00652092,
     author = {Campillo, Fabien and Rakotozafy, Rivo},
     title = {{MCMC} for non linear/non {Gaussian} state-space models: Application to fishery stock assessment},
     journal = {HAL},
     volume = {2004},
     number = {0},
     year = {2004},
     language = {en},
     url = {http://dml.mathdoc.fr/item/hal-00652092}
}

Campillo, Fabien; Rakotozafy, Rivo. {MCMC} for non linear/non {Gaussian} state-space models: Application to fishery stock assessment. HAL, Tome 2004 (2004) no. 0, . http://gdmltest.u-ga.fr/item/hal-00652092/