STATISTICAL BAYESIAN ANALYSIS OF EXPERIMENTAL DATA.

Authors

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

Keywords:

Bayesian analysis, Markov Chain Monte Carlo Algorithms, regression models

Abstract

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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Author Biographies

AHLAM LABDAOUI, University Constantine 1

Department of Mathematics

HAYET MERABET, University Constantine 1

Department of Mathematics

References

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Published

2012-12-01

How to Cite

LABDAOUI, A., & MERABET, H. (2012). STATISTICAL BAYESIAN ANALYSIS OF EXPERIMENTAL DATA. Sciences & Technology. A, Exactes Sciences, (36), 31–39. Retrieved from https://revue.umc.edu.dz/a/article/view/2024

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