Introduction to Bayesian Computing and Techniques

taught by Peter Congdon

Participants in this course will learn why Bayesian computing has gained wide popularity, and how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.

Statistical analysts and consultants who need to make decisions (or advise decision-makers) via a process that incorporates domain-specific information -- not simply abstract and arbitrary statistical rules.

Introduction to Bayesian Computing and Techniques

taught by Peter Congdon

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Introduction to Bayesian Computing and Techniques

taught by Peter Congdon

Participants in this course will learn why Bayesian computing has gained wide popularity, and how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software. Participants will learn how to use WinBUGS software, use it to estimate parameters of standard distributions, and implement simple regression models.

See also: Introduction to Bayesian Statistics (which ideally should precede this course) and Bayesian Regression Modeling via MCMC Techniques(which ideally should follow this course.)

• Introduction to Bayesian Statistics

SESSION 1:

• Basic ideas of MCMC
• Benefits of Bayes methods
• Priors and Prior Informativeness
• Important distributions in Bayesian analysis
• Introduction to three standard schemes: (normal data, normal prior; binomial data, beta prior; poisson data, gamma prior)

• Winbugs syntax and programs, data inputs, convergence checks, obtaining summaries

• Main elements of posterior summarization
• Tests on parameters or parameter collections (posterior probability tests)
• Model predictions

• Simple regression models (normal, binary & binomial, poisson)

HOMEWORK:

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

This course takes place over the internet 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.

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.

Extensive course notes are provided, and the course may be followed without purchasing a text.

For a more in-depth experience, and for those who require a book for reference, we recommend A First Course in Bayesian Statistical Methods, by Hoff, P., which can be ordered directly from the publisher here. Springer offers a generous discount on this book after providing the code AECT15 (this code is case sensitive) in the Promotion Code field when prompted during checkout time if you are from North or South America. The same code will work for the rest of the world if you order from the North American site, but may result in longer ship time and higher ship cost (alternatively, you can buy from local site with no discount.)

If you already have Bayesian Modeling Using WinBUGS, by I. Ntzoufras (2008, Wiley), that book is also a useful companion to this course, especially for Lesson 2. It can be ordered from Wiley by clicking here. 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 course will be based on the freeware WinBUGS, fitting distributions to datasets. Click here for more information.

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