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Bayesian Regression Modeling via MCMC Techniques



April 25, 2014 to May 23, 2014 October 24, 2014 to November 21, 2014 April 24, 2015 to May 22, 2015 October 23, 2015 to November 20, 2015

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Bayesian Regression Modeling via MCMC Techniques

taught by Peter Congdon

Aim of Course:

Participants in this course will learn how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS and R software. Topics covered include Gibbs sampling and the Metropolis-Hastings method. Participants will also learn how to implement linear regression (normal and t errors), poisson and loglinear regression, and binary/binomial regression using WinBUGS.

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

Course Program:

WEEK 1: Using Markov Chain Monte Carlo

  • Monte Carlo vs MCMC
  • Estimating parameters and probabilities from complex models
  • Sampling from random variables
  • Gibbs sampling & full conditional densities
  • Convergence
  • Metropolis-Hastings method

Preview the instructor's notes for Lesson 1.

WEEK 2: Sampling From Priors

  • Sampling from standard densities
  • Specifying priors and likelihoods
  • Assessing convergence
  • Estimating parameters, probabilities and other model based quantities: Case Studies
  • Posterior summaries

WEEK 3: Linear Regression Modeling in WinBUGS

  • Linear regression model in WinBUGS
  • Setting priors on regression coefficients and residual variances
  • Predictor selection
  • Extending the Normal linear model (outliers, heteroscedasticity)

WEEK 4: General Linear Modeling in WinBUGS

  • Logistic regression for binary and binomial responses; using other links
  • Poisson regression
  • Latent data approach for binary regression
  • Loglinear models for contingency tables


Homework in this course consists of short answer questions to test concepts and guided data analysis using software.

In addition to assigned readings, this course also has supplemental readings available online, end of course data modeling project, and example software codes.

Bayesian Regression Modeling via MCMC Techniques

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


April 25, 2014 to May 23, 2014 October 24, 2014 to November 21, 2014 April 24, 2015 to May 22, 2015 October 23, 2015 to November 20, 2015

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.)


Have you reviewed the REQUIREMENTS for this course?

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

Bayesian Regression Modeling via MCMC Techniques

taught by Peter Congdon

Who Should Take This Course:

Statisticians and analysts who need to build statistical models of data.



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.

This course has Supplemental readings that are available online, and an end of the year course data modeling project.

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 required text for this course is Bayesian Statistical Modeling, 2nd edition, by Peter Congdon. Wiley typically offers statistics.com customers up to 15% discount on this book (and all other statistics titles): enter the code aff15 in the Promotion Code field when prompted during checkout and click the Apply Discount button. (If you are located in Asia, the web procedure for your location may not accept this discount – try calling your regional Wiley representative.). The text is also available as an "e-book".



Selected R programs will be used in week 1, but the primary program used will be the freeware WinBUGS program for Gibbs Sampling; click here for more information.

(We recommend that you download and install prior to the course start date.)

Though the R programs used in week 1 will not require a high degree of familiarity with R, if you want to use R with this course, you should have some prior experience and facility with it. Help from the TA will be available, but limited.

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