IIMA

Prof. Karthik Sriram

Indian Institute of Management Ahmedabad

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The codes below help learn and implement Bayesian Linear Regression in R.

Part 1

 Here, we start with ordinary least square regression and then formulate and solve Bayesian linear regression with Zellner's g-prior. 

click here for the  Rcode. The R code uses the data example pbc.vote from BaM package of R:

Click here to download csvfile containing the data.

Click here to dowload the pdf containing description of variables in the data.


Part 2

We demonstrate how the ideas of part 1 can be easily extended to modeling non-linear functional relationships.

click here for the Rcode.


Part 3

In part 2, we used cross validation criteria to determine the smoothing parameter.  Here, we demonstrate a Bayesian formulation to determine the smoothing parameter.

click here for the Rcode


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