Bayesian Computing

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Introduction to Bayesian Computing and Techniques

taught by Peter Congdon

Aim of Course:

In this online course, "Introduction to Bayesian Computing and Techniques" students will learn why Bayesian computing has gained wide popularity, and how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling the BUGS package (WinBUGS/OPENBUGS). Participants will learn how to use BUGS software, use it to estimate parameters of standard distributions, and implement simple regression models.

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

Course Program:

WEEK 1: Introduction to Bayesian MCMC

  • 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)

WEEK 2: Bayesian Programming in BUGS

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

WEEK 3: Bayesian Posteriors

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

WEEK 4: Bayesian Regression

  • 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.

In addition to assigned readings, this course also has supplemental readings available online.


Introduction to Bayesian Computing and Techniques

Who Should Take This Course:

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.

Level:

intermediate


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.

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.

Time Requirement:
About 15 hours per week, at times of  your choosing.

this course has supplemental readings that are vailable online

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,  CEU's and a record of course completion will be issued by The Institute, upon request.
This course is also recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam, and can help CAP®analysts accrue Professional Development Units to maintain their certification .


Course Text:

Extensive course notes are provided, and, particularly for those focusing on the implementation of techniques in Winbugs, the course may be followed without purchasing a text.

For a more in-depth look at the underlying concepts, and for those who require a book for reference, we recommend A First Course in Bayesian Statistical Methods, by Peter Hoff.

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.

Software:

The course will be based on the freeware WinBUGS, fitting distributions to datasets.


Introduction to Bayesian Computing and Techniques

Instructor(s):
Dates:
February 24, 2017 to March 24, 2017August 25, 2017 to September 22, 2017February 23, 2018 to March 23, 2018August 24, 2018 to September 21, 2018

Course Fee: $589