WorkshopsPractical Bayesian Computation with SAS (3) and Q&A
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This one-day tutorial course reviews the basic concepts of Bayesian inference and focuses on the practical use of Bayesian computational methods. The objectives are to familiarize statistical programmers and practitioners with the essentials of Bayesian computing, and to equip them with computational tools in SAS through a series of worked-out examples that demonstrate sound practices for a variety of Bayesian concepts. The first part of the course reviews differences between classical and Bayesian approaches to inference, fundamentals of prior distributions, and concepts in estimation. The course also covers MCMC methods and related simulation techniques, emphasizing the interpretation of convergence diagnostics in practice. The second part of the course introduces Bayesian computation and illustrates the Bayesian treatment of several statistical models. This part of the course will look at topics such as sensitivity analysis, prediction, model assessment and selection, multivariate analysis, and so on. The examples are done using SAS, with code explained in detail. Attendees should have a background equivalent to an M.S. in applied statistics. Previous exposure to Bayesian methods or SAS software is useful but not required. Familiarity with material at the level of this text book is appropriate: Probability and Statistics (Addison Wesley), DeGroot and Schervish.
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Fang Chen
2012-07-19
14:30:00 - 16:00:00
408 , Liberal Education Classroom Building
This one-day tutorial course reviews the basic concepts of Bayesian inference and focuses on the practical use of Bayesian computational methods. The objectives are to familiarize statistical programmers and practitioners with the essentials of Bayesian computing, and to equip them with computational tools in SAS through a series of worked-out examples that demonstrate sound practices for a variety of Bayesian concepts. The first part of the course reviews differences between classical and Bayesian approaches to inference, fundamentals of prior distributions, and concepts in estimation. The course also covers MCMC methods and related simulation techniques, emphasizing the interpretation of convergence diagnostics in practice. The second part of the course introduces Bayesian computation and illustrates the Bayesian treatment of several statistical models. This part of the course will look at topics such as sensitivity analysis, prediction, model assessment and selection, multivariate analysis, and so on. The examples are done using SAS, with code explained in detail. Attendees should have a background equivalent to an M.S. in applied statistics. Previous exposure to Bayesian methods or SAS software is useful but not required. Familiarity with material at the level of this text book is appropriate: Probability and Statistics (Addison Wesley), DeGroot and Schervish.