Students who complete this course will gain a basic understanding of MCMC and the benefits of Bayes methods. They will explore how to build Bayesian models using BUGS, main elements of Bayesian posteriors, and commonly used Bayesian regression models.
- Explain the benefits of Bayesian methods, and the basic idea of MCMC
- Install BUGS software and be able to write BUGS code
- Conduct Bayesian regression
- Work with 3 standard schemes for prior and posterior distributions
- Conduct posterior summaries and tests
- Make predictons from models
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.
Dr. Peter Congdon is a Research Professor in Quantitative Geography and Health Statistics at Queen Mary University of London. He is the author of several books and numerous articles in peer-reviewed journals. His research interests include spatial data analysis, Bayesian statistics, latent variable models, and epidemiology.
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)
Bayesian Programming in BUGS
- BUGS 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
- Commonly-used regression models (normal, binary & binomial, poisson)
Instructors: Dr. Peter Congdon
Instructors: Dr. Peter Congdon
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In addition to assigned readings, this course also has supplemental readings available online.
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.
The course will be based on the freeware BUGS package (WinBUGS/OPENBUGS).
Course Fee & Information
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.
Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.
Contact us to get information on group rates.
Academic affiliation? In most courses you are eligible for a discount at checkout. Use promo code ACADEMIC where prompted during registration.
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