Bayesian statistical methods have gained considerably in power, scientific relevance, and applicability over the last 10 years, with the widespread adoption of simulation-based computation methods involving Markov chain Monte Carlo techniques as the engine of progress. Cutting-edge Bayesian modeling is now going on in a wide variety of fields, including bioinformatics, computer science, engineering, epidemiology, machine learning, and statistics. Interdisciplinary transfer of ideas is important but increasingly difficult to achieve, as the literatures in each field burgeon and the use of different terms for similar concepts hampers the ability of search engines to help workers in one field keep up-to-date in other disciplines. Bayesian methodology is important to study in its own right, but for greatest scientific relevance (1) careful attention to modeling and (2) the need for methodology to be appropriate to context are also crucial.
In this project the Statistics Group within the newly-forming Department of Applied Mathematics and Statistics (AMS) at the University of California, Santa Cruz (UCSC), will address these issues by hosting an International Workshop on Bayesian Data Analysis at UCSC from Friday through Sunday 8-10 August 2003, as a kind of satellite meeting to be held right after the Joint Statistical Meetings (JSM) nearby in San Francisco, CA, from August 3--7, 2003. Our objectives are to bring together about 100 people interested in Bayesian modeling and data analysis from a wide variety of disciplines around the world, including about 30 invited participants (leading Bayesian researchers) as speakers and discussants, to promote multidisciplinary discussion of common problems and interdisciplinary transfer of ideas (many of these people will be young researchers). The focus of the Workshop will be Bayesian data analysis: starting with a real problem in science or decision-making, formulating this problem in statistical terms, using Bayesian methods to solve the original problem, and discussing the strengths and weaknesses of the solution both statistically and substantively, with plenty of attention to the interplay between the real-world context and the Bayesian model building, checking, and reformulating. NSF funding will be principally used to provide partial travel and local expenses support for young researchers, minorities, and other under-represented groups.
