The First Workshop in Bayesian Statistics and Machine Learning, to be held at Carnegie Mellon University, October 15-17, 2009, will hear extended discussion of the following case studies: "Decision theoretic Bayesian nonparametric inference for the molecular characterization and stratification of colorectal cancer using genome-wide arrays", by Christopher C. Holmes, Christopher Yau, Ian Tomlinson and Jean-Baptiste Cazier; "Calibrating the Universe: A Bayesian Uncertainty Analysis of Galaxy Simulation" by Ian Vernon, Richard Bower and Michael Goldstein; "Rigorous Error Analysis for Small Angle Neutron Scattering Datasets Using Bayesian Inference", by Chip Hogg, Jay Kadane, Jong Soo Lee, and Sara Majetich. A new investigator's session, a short-course introduction to Bayesian analysis and machine learning, a session on how to get a grant, and a mixer, are all aimed at attracting students and recent graduates.
This grant supports travel and local expenses for speakers and young investigators to attend the First Workshop in Bayesian Statistics and Machine Learning, to be held at Carnegie Mellon University, October 15-17, 2009. The fields of Bayesian statistics and machine learning are starting to grow together; we hope that the workshop will accelerate this process by focusing attention on serious scientific applications of the methods.
