statistical bayesian analysis of experimental data.

Clicks: 149
ID: 217955
2012
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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.
Reference Key
labdaoui2012sciencesstatistical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;AHLAM LABDAOUI;HAYET MERABET
Journal journal of big data
Year 2012
DOI
DOI not found
URL
http://revue.umc.edu.dz/index.php/a/article/view/2024
Keywords
bayesian analysis

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