Get your hands on Bayesian computing with Peter Congdon's online course "An Introduction to Bayesian Computing and Techniques" at Statistics.com. For more details please visit at http://www.statistics.com/BayesianComputing/.
Participants in "An Introduction to Bayesian Computing and Techniques" 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. This course is recommended as a lead-in to "Bayesian Regression Modeling via MCMC," also with Dr. Congdon.
- 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)
You will be able to ask questions and exchange comments with Dr. Michael Chernick and Dr. Robert LaBudde via a private discussion board throughout the course. The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week. Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.