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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.
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https://hal.inria.fr/hal-00999966
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Submitted on : Monday, May 23, 2016 - 4:02:12 PM
Last modification on : Tuesday, February 9, 2021 - 3:20:09 PM

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

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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⟩

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