STATISTICAL BAYESIAN ANALYSIS OF EXPERIMENTAL DATA.

Auteurs-es

  • AHLAM LABDAOUI University Constantine 1
  • HAYET MERABET University Constantine 1

Mots-clés :

Bayesian analysis, Markov Chain Monte Carlo Algorithms, regression models

Résumé

The Bayesian researcher should know the basic ideas underlying Bayesian methodology and the computational tools used in modern Bayesian econometrics.  Some of the most important methods of posterior simulation are Monte Carlo integration, importance sampling, Gibbs sampling and the Metropolis- Hastings algorithm. The Bayesian should also be able to put the theory and computational tools together in the context of substantive empirical problems. We focus primarily on recent developments in Bayesian computation. Then we focus on particular models. Inevitably, we combine theory and computation in the context of particular models. Although we have tried to be reasonably complete in terms of covering the basic ideas of Bayesian theory and the computational tools most commonly used by the Bayesian, there is no way we can cover all the classes of models used in econometrics. We propose to the user of analysis of variance and linear regression model.

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Bibliographies de l'auteur-e

AHLAM LABDAOUI, University Constantine 1

Department of Mathematics

HAYET MERABET, University Constantine 1

Department of Mathematics

Références

Agresti A. Categorical Data Analysis. (2002).

Anas Altaleb, Christian P. Robert, Analyse bayésienne du modèle logit : algorithme par tranches ou Metropolis-Hastings?, revue de statistique appliquée, tome 49, n°4 (2001), p. 53-70.

Christian P. Robert, Jean-Michel Marin, Bayesian Core: A Practical Approach to Computational Bayesian Statistics

Christian P, Robert and George Casella, Monte Carlo Statistical Methods, Springer, (2004).

C. Robert et G. Casella, Monte Carlo Statistical Methods, Springer, 2nd edition, (2004).

David J. Lunn, Andrew Thomaa, Nicky Best and David Spiegelhalter WinBUGS – A Bayesian modeling framework: Concepts, structure, and extensibility, Statistics and Computing (2000) 10, 325–337.

Éric. Parent. Jacques Bernier, Le raisonnement bayésien, Springer-Verlag France, Paris, (2007).

Lionel Riou França, statistique bayésienne, INSERM U669, Mai 2009.

Robert, C.P. and Casella, G. Monte Carlo Statistical Methods. New York: Springer Verlag (1999).

Robert, C.P. L’analyse statistique bayésienne Economica, Paris (1992).

Ton J Cleophas, Aeilko H Zwinderman, Toine F Cleophas, Statistics Applied to Clinical Trials (2006).

Publié-e

2012-12-01

Comment citer

LABDAOUI, A., & MERABET, H. (2012). STATISTICAL BAYESIAN ANALYSIS OF EXPERIMENTAL DATA. Sciences & Technologie. A, Sciences Exactes, (36), 31–39. Consulté à l’adresse https://revue.umc.edu.dz/a/article/view/2024

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