Date:         Tue, 15 Sep 1998 13:56:38 -0400
Reply-To:     "Barron, Alfred [PRI]" <ABARRON@PRIUS.JNJ.COM>
Sender:       "SAS(r) Discussion" <SAS-L@UGA.CC.UGA.EDU>
From:         "Barron, Alfred [PRI]" <ABARRON@PRIUS.JNJ.COM>
Subject:      Applied Bayes Short Course
Comments: To: edstat-l <edstat-l@jse.stat.ncsu.edu>,
          stat-l <stat-l@vm1.mcgill.ca>
Content-Type: text/plain

>              Princeton-Trenton and New Jersey Chapters, ASA
>                            will present a Short Course on
>
>                     APPLIED BAYESIAN DATA ANALYSIS
>
>              Instructor:    Prof. Brad Carlin, Univ. of Minnesota
>                                 (http://muskie.biostat.umn.edu/~brad)
>
>              Date:           Monday, October 19, 1998
>
>              Location:     Bristol-Myers Squibb Pharm. Resch. Inst.
>                                One Squibb Drive
>                                Building 111, Room A, B, C
>                                New Brunswick, NJ
>
>              Time:          8:30 AM to 4:30 PM
>
>              Cost:          $50 for Prince-Tret. and NJ Chapter members
>                                if received before Sept 28, and $65 after;
>                                $25 for full time students (+
> verification);
>                                $65 for all others if received before Sept
> 28,
>                                and $80 after.
>
>              Includes:     Breakfast, lunch, course notes. Please note
>                                that the textbook, 'Bayes and Empirical
> Bayes
>                                Methods for Data Analysis, by Carlin &
> Louis,
>                                is optional and available for $58.00 only
> with
>                                preregistration by Sept. 28.
>
>                                                ABSTRACT
>
>              Bayes and Empirical Bayes methods enable the combining
>              of information from similar and independent experiments
>              yielding improved inference for both individual and shared
>              model characteristics. As a result of recent advances in
>              computing and the consequent ability to evaluate complex
>              models, Bayesian methods have increased in popularity in
>              the analysis of data.
>
>              This course introduces Bayes and Empirical Bayes methods,
>              demonstrates their usefulness in challenging settings, such
>              as biostatistics, and shows how they can be implemented
>              using modern Markov Chain Monte Carlo computational
>              methods. BUGS, the best MCMC software available, will
>              also be described and illustrated.
>
>              Further information may be viewed at:
>
>            http://www.amstat.org/chapters/princetontrenton   or
>                   http://www.stat.rutgers.edu/njasa
>
>              or contact Jim Kenyon at [732] 519-2502, or by email at
>              jkenyon@usccmail.bms.com  .
>
>           - Julia Wang, R.W. Johnson Pharm. Research Institute
>             Chair, Program Committee, NJ/ASA
>
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