Bayesian Regression Modeling via MCMC Techniques

Bayesian Regression Modeling via MCMC Techniques

taught by Peter Congdon

Aim of Course:

In this online course, "Bayesian Regression Modeling via MCMC Techniques" students 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: Specifying a model and Computing Options

  • R implementations of BUGS (R2OpenBUGS, nimble)
  • Sampling from standard densities
  • Specifying priors and likelihoods to define a model
  • Assessing convergence
  • 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

Who Should Take This Course:
Statisticians and analysts who need to build statistical models of data.

Participants should have had some exposure to Bayesian computing (such as that provided in An Introduction to Bayesian Computing and Techniques), plus some familiarity with generalized linear models (such as that provided in Generalized Linear Models).

Organization of the Course:

This course has Supplemental readings that are available online, and a final data modeling project.

Options for Credit and Recognition:

Specializations are an easy way for you to demonstrate mastery of a specific skill in statistics and analytics. This course is part of the Bayesian Statistics Specialization which uses Bayes' Theorem to perform analyses and computations, and learn what makes it so popular.  Take any three of the five courses on this topic (this course, plus the courses listed to the right under "related courses," not including conferences).  For savings, use the promo code "bayes-specialization" and register for all three courses at once for  $1197 ($399 per course, not combinable with other tuition savings).  If you register for all five, you'll still receive the discounted rate.

Course Text:

The required text for this course is Bayesian Statistical Modeling, 2nd edition, by Peter Congdon.



Selected R programs will be used in week 1, but the primary program used will be the freeware BUGS package (WinBUGS/OPENBUGS)

(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 continue to use R with the entire course, you should have some prior experience and facility with it. 



April 19, 2019 to May 17, 2019 April 17, 2020 to May 15, 2020

Bayesian Regression Modeling via MCMC Techniques


April 19, 2019 to May 17, 2019 April 17, 2020 to May 15, 2020

Course Fee: $589

Do you meet course prerequisites? What about book & software? (Click here to learn more)

We have flexible policies to transfer to another course, or withdraw if necessary (modest fee applies)

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First time student or academic? Click here for an introductory offer on select courses. Academic affiliation?  You may be eligible for a discount at checkout.

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