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.
- 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
March 25, 2016 to April 22, 2016
About 15 hours per week, at times of your choosing.
INR 37,740 (assuming $ = INR 60)
Part Time/Full Time:
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.
- Peter Congdon