Computational probability modeling and Bayesian inference
Résumé
Computational probabilistic modeling and Bayesian inference has met a great success over the past fifteen years through the development of Monte Carlo methods and the ever increasing performance of computers. Through methods such as Monte Carlo Markov chain and sequential Monte Carlo Bayesian inference effectively combines with Markovian modelling. This approach has been very successful in ecology and agronomy. We analyze the development of this approach applied to a few examples of natural resources management.
Mots clés
MODELISATION COMPUTATIONNELLE MARKOVIENNE
INFERENCE BAYESIENNE COMPUTATIONNELLE
MODELISATION BAYESIENNE HIERARCHIQUE
METHODE DE MONTE CARLO PAR CHAINE DE MARKOV
METHODE DE MONTE CARLO SEQUENTIELLE
ECOLOGIE NUMERIQUE
COMPUTATIONAL MARKOVIAN MODELING
COMPUTATIONAL BAYESIAN INFERENCE
HIERARCHICAL BAYESIAN MODELING
MONTE CARLO MARKOV CHAIN
SEQUENTIAL MONTE CARLO
COMPUTATIONAL ECOLOGY
ECOLOGIE