Case Studies in Bayesian Statistics 2009

Main Invited Papers

Deadline for submissions: January 26, 2009

We are calling for proposals in the form of detailed abstracts (about 2 pages) from those interested in presenting one of the main invited papers for discussion. To be considered for a presentation, abstracts are due by Monday, February 1, 2009. Abstracts should emphasize scientific and technological background, and should clarify the extent to which the statistical work will address key components of the problems articulated. They should also include statements that make clear the amount of work that will be accomplished by the time the manuscripts are due, and clearly identify the collaborators, particularly the non-statisticians, who will be involved in the presentation. Case studies to be presented at the meeting will be selected by the organizing committee on the basis of all abstracts received.

Abstracts should be submitted via the workshop web site. Questions can be sent to: bayes at stat.cmu.edu. Additional information may be obtained by contacting Jay Kadane at kadane at stat.cmu.edu; phone: 412-268-2718; FAX: 412-268-7828; or by contacting any of the organizers.

Online Submission of Abstracts

Listed below is the format for abstracts for main invited papers. You may edit the text inside the window below and then press SUBMIT to send it. You should replace the sample information with the correct information. Confirmation of receipt of the abstract will be sent to the email address you provide.

Email:

\documentclass{article} \pagestyle{empty} \begin{document} % % Please list any A/V requirements as comments within this document % \begin{center} {\Large {\bf TITLE HERE}} \vspace{.2in} by\\ {\em AUTHOR'S NAME(S) HERE}\\ {\em AFFILIATION HERE}\\ {\em ADDRESS HERE}\\{\em ADDRESS HERE}\\ {\tt EMAIL ADDRESS HERE} \end{center} \begin{abstract} {ABSTRACT HERE - In the Abstract please avoid unnecessary mathematical detail, and,in particular avoid displayed equations. You should aim for your Abstract to be as attractive to a future audience as possible. Relatively simple mathematics is acceptable, such as, $\phi_q$-criteria and $z = g(x)'\theta$.} \end{abstract} %%%main-please do not remove this line of text%% \end{document}

Case Studies in Bayesian Statistics and
Machine Learning

Workshop 1 - 2009

Case Studies in Bayesian Statistics and Machine Learning

Workshop 1 - 2009

Case Studies in Bayesian Statistics and
Machine Learning