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INTRODUCTION TO BAYESIAN TECHNIQUES

Bayesian statistics has the advantage, in comparison to traditional statistics, which is not founded on Bayes’ theorem, of being easily established and derived. Intuitively, methods become apparent which in traditional statistics give the impression of arbitrary computational rules. Furthermore, problems related to testing hypotheses or estimating confidence regions for unknown parameters can be readily tackled by Bayesian statistics. The reason is that by use of Bayes’ theorem one obtains probability density functions for the unknown parameters. These density functions allow for the estimation of unknown parameters, the testing of hypotheses and the computation of confidence regions. Therefore, application of Bayesian statistics has been spreading widely in recent times.

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The dropbox folder contains exercises, research papers and industry cases that will be covered.

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                                   Download here.                                                                            Download here.

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 I have a blog titled review of different softwre packages. 

Module 0: An Introduction to Bayesian Techniques

Module 1: Review of the Important Probability Distribution and Kernel of the PDF

Module 2: Introduction to Bayesian Model Comparison

Module 3: Introduction to Marginal Likelihood Function in Bayesian Model Comparison

Module 4: Bayesian Model Averaging

Module 5: Choosing the Prior Density

Module 6: The Linear Regression Model

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