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Bayesian Statistics in R

Instructor(s):

Dates:

September 26, 2014 to October 24, 2014

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Bayesian Statistics in R

taught by Peter Congdon

Aim of Course:

After taking this 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.

This course may be taken individually (one-off) or as part of a certificate program.

Course Program:

WEEK 1: 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


WEEK 2: Linear Regression with rjags

  • Specifying Models
  • Specifying Priors on Regression Coefficients and Residual Variances
  • Posterior Summarisation in R


WEEK 3: Regression for Count, Binary and Binomial Data

  • Poissonregression
  • Logit and Probit Regression
  • Negative Binomial Regression


WEEK 4:  Other Regression Techniques

  • Ordinal and multinomial regression
  • Categorical predictors
  • Predictor selection


HOMEWORK:

Bayesian Statistics in R

Be sure you meet all of the minimum requirements before you register, click here to learn more.

Instructor(s):

Dates:
September 26, 2014 to October 24, 2014

Course Fee: $629

Tuition Savings:  When you register online for 3 or more courses, $200 is automatically deducted from the total tuition. (This offer cannot be combined and is only applicable to courses of 3 weeks or longer.)


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Bayesian Statistics in R

taught by Peter Congdon

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.   Note:  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.

Level:

Prerequisite:
These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.
Organization of the Course:

This course takes place online at the Institute for 4 weeks. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

The course typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.


Credit:
Students come to the Institute for a variety of reasons. As you begin the course, you will be asked to specify your category:
  1. You may be interested only in learning the material presented, and not be concerned with grades or a record of completion.
  2. You may be enrolled in PASS (Programs in Analytics and Statistical Studies) that requires demonstration of proficiency in the subject, in which case your work will be assessed for a grade.
  3. You may require a "Record of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's).  For those successfully completing the course, 5.0 CEU's and a record of course completion will be issued by The Institute, upon request.

Course Text:
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.

Software:

JAGS (Just Another Gibbs Sampler)

The R-INLA project

The course will focus on use of RJAGS (and secondarily R-INLA). An rjags implementation in R rests crucially on coding in JAGS, which is virtually identical to BUGS.


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