Fabien Campillo ; Rivo Rakotozafy ; Vivien Rossi - Computational probability modeling and Bayesian inference

arima:1917 - Revue Africaine de Recherche en Informatique et Mathématiques Appliquées, November 9, 2008, Volume 9, 2007 Conference in Honor of Claude Lobry, 2008 - https://doi.org/10.46298/arima.1917
Computational probability modeling and Bayesian inference

Authors: Fabien Campillo ORCID-iD1; Rivo Rakotozafy 2; Vivien Rossi ORCID-iD3

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.

Volume: Volume 9, 2007 Conference in Honor of Claude Lobry, 2008
Published on: November 9, 2008
Submitted on: April 13, 2008
Keywords: Computational Markovian modeling,Computational Bayesian inference,Hierarchical Bayesian modeling,Monte Carlo Markov chain,Sequential Monte Carlo,Computational ecology, Modélisation computationnelle markovienne,inférence bayésienne computationnelle,modélisation bayésienne hiérarchique,méthode de Monte Carlo par chaîne de Markov,méthode de Monte Carlo séquentielle,écologie numérique,[MATH.MATH-PR] Mathematics [math]/Probability [math.PR],[INFO] Computer Science [cs],[MATH] Mathematics [math]

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Source : ScholeXplorer IsRelatedTo DOI 10.1017/s0266466600006794
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Markov Chain Monte Carlo Simulation Methods in Econometrics

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