Skip to Main content Skip to Navigation
Journal articles

Computational probability modeling and Bayesian inference

Abstract : 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.
Complete list of metadata

Cited literature [41 references]  Display  Hide  Download
Contributor : Coordination Episciences Iam <>
Submitted on : Monday, May 23, 2016 - 4:02:12 PM
Last modification on : Tuesday, February 9, 2021 - 3:20:09 PM


Explicit agreement for this submission


  • HAL Id : hal-00999966, version 2
  • PRODINRA : 158057


Fabien Campillo, Rivo Rakotozafy, Vivien Rossi. Computational probability modeling and Bayesian inference. Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées, INRIA, 2008, Conference in Honor of Claude Lobry, 9, pp.123-143. ⟨hal-00999966v2⟩



Record views


Files downloads