2014 SAS統計軟體貝氏分析短期課程活動公告

※課程內容勘正,此次課程主題僅有「Practical Bayesian Computation using PROC MCMC」。

統計教學中心及台大數學科學中心將於103年12月23日(星期二),共同舉辦SAS統計軟體短期課程,邀請到SAS公司資深研究統計師陳方博士蒞臨演講,課程主題為「Practical Bayesian Computation using PROC MCMC」。

講者:Dr. Fang Chen (陳方 博士)
      Senior Research Statistician at SAS Institute Inc.
時間:2013年12月23日星期二 上午 9:00 ~ 12:00

講題:Practical Bayesian Computation using PROC MCMC


This tutorial is the second part of a two-part series on Bayesian analysis and the objectives are to equip students with computational tools through a series of worked-out examples that demonstrate sound practices for a variety of statistical models and Bayesian concepts.

The MCMC procedure is a general purpose Markov chain Monte Carlo simulation tool designed to fit a wide range of Bayesian models, including linear or nonlinear models, multi-level hierarchical models, and models with nonstandard likelihood function or prior distributions. This three-hour tutorial provides a quick introduction to PROC MCMC and demonstrates its use with a series of applications, such as Monte Carlo simulation, various regression models, sensitivity analysis, random-effects models, and predictions. In addition, topics that are commonly encountered are also discussed, including survival analysis, network meta-analysis, power prior, and missing data problems.

This tutorial is intended for students who are interested in Bayesian computation. Attendees should have a basic understanding of Bayesian methods and experience using the SAS language. This tutorial is based on SAS/STAT 13.2.

 * Outline

1) A Primer on PROC MCMC

2) Monte Carlo Simulation

3) Generalized Linear and Nonlinear Regression Models

4) Inference on Functions of Parameters

5) Posterior Predictive Distribution

6) Multivariate Analysis

7) Power Prior

8) Random-effects Models

* Generalized Linear Models

* Repeated Measurements (Network Meta-Analysis)

9) Missing Data Analysis

* Missing at Random (MAR)

* Missing not at Random (MNAR)

10) Survival Analysis

* Piecewise Exponential with Frailty (optional)





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