After taking this course you will be able to install and run RJAGS, a program for Bayesian analysis within R. You will learn how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data.
- Write code in rjags
- Specify models for linear regression
- Specify models for count, binary and binomial data
- Incorporate categorical predictors into models
- Implement algorithms to select predictors
Who Should Take This Course
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.
In this course you will learn both BUGS coding and how to integrate it into R. If you are not familiar with BUGS, and want to take the time to learn BUGS first, consider taking the optional prerequisite listed below.
Using RJAGS for Bayesian inference in R: Introductory Ideas and Programming Considerations
- Basic Principles of Bayesian Inference and MCMC Sampling
- R and RJAGS for Bayesian inference. Initial values, posterior summaries, checking convergence.
- JAGS and BUGS programming Syntax, with simple applications
Linear Regression with RJAGS
- Specifying Models
- Specifying Priors on Regression Coefficients and Residual Variances
- Posterior Summarisation in R
Regression for Count, Binary, and Binomial Data
- Poisson Regression
- Logit and Probit Regression
- Negative Binomial Regression
Other Regression Techniques
- Ordinal and multinomial regression
- Categorical predictors
- Predictor selection
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The homework in this course consists of short answer questions to test concepts, guided exercises in writing code and guided data analysis problems using software.
This course has example software codes and supplemental readings available online, and has an end-of-course project.
The BUGS Book – A Practical Introduction to Bayesian Analysis, David Lunn et al. CRC Press (2012).
Note: This book is an excellent guide to BUGS. It is not specifically about R, but all required instruction about R coding will be provided in the course materials. If you are already well familiar with BUGS and have your own reference, you may not need this book.
Literacy, Accessibility, and Dyslexia
At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:
- Color Enhancer (for colorblindness)
- HelperBird (for colorblindness, dyslexia, and reading difficulties)
- Mobile Dyslexic
- Color Vision Simulation (native accessibility feature)
- Other native accessibility features instructions