Bayesian Statistics in R – Statistics.Com

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After taking this online course, you will be able to install and run rjags, a program for Bayesian analysis within R.  Using R and rjags, you will learn how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data.   Procedures covered from a Bayesian perspective include linear regression, Poisson, logit and negative binomial regression, and ordinal regression.

Course Program:

  • Week 1: Using rjags for Bayesian inference in R: Introductory Ideas and Programming Considerations
  • Week 2: Linear Regression with rjags
  • Week 3: Regression for Count, Binary and Binomial Data
  • Week 4: Other Regression Techniques

Important Date:

March 25, 2016 to April 22, 2016

Duration:

4 Weeks

Time Requirement:

About 15 hours per week, at times of  your choosing.

Fees:

INR 37,740 (assuming $ = INR 60)

Part Time/Full Time:

Part Time

Pre-requisites:

You should take this course if you are familiar with R and with Bayesian statistics at the introductory level, and work with or interpret statistical models and need to incorporate Bayesian methods.  Analysts who need to incorporate their work into real-world decisions, as opposed to formal statistical inference for publication, will be especially interested.  This includes business analysts, environmental scientists, regulators, medical researchers, and engineers.

  • R
  • JAGS
  • Peter Congdon
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