CASE STUDIES IN BAYESIAN STATISTICS AND MACHINE LEARNING

Carnegie Mellon University, Pittsburgh PA

Investigators

Abstract

Case Studies in Bayesian Statistics and Machine Learning continues the tradition of the workshop series Case Studies in Bayesian Statistics with a meeting October 14-15, 2011. The usual format for meetings and workshops in statistics and computer science emphasize methods over applications, which often stifles discussion about the impact of the methods on the substantive problem. The unique format of the case studies workshop series has allowed for substantive discussion of application-specific issues, most importantly a narrative of how the substantive scientific problem demanded either new methods or the novel application of existing approaches, the obstacles in the real problem that the researchers encountered, and the solutions that resulted. Statistics and machine learning provide essential methodologies throughout the sciences, yet the connection between the science and the data analytic problem formulation is rarely emphasized in traditional conferences. This workshop fosters cross-pollination of ideas from the statistics and computer science ommunities and promotes work that takes on important challenges in scientific investigation. The case studies highlight the way novel application of statistical machine learning methods are used to answer a scientific question. The workshop especially supports efforts by young investigators, in part by including a session of presentations exclusively by younger investigators.