Bayesian approaches to data analysis sometimes offer important advantages over classical methods. This collection of papers from a workshop on Bayesian statistics discuss important research problems and show the advantages of a Bayesian approach.

Invited Papers.- School Choice in NY City: A Bayesian Analysis of an Imperfect Randomized Experiment.- Adaptive Bayesian Designs for Dose-Ranging Drug Trials.- Modeling the Impact of Traffic-Related Air Pollution on Childhood Respiratory Illness.- Contributed Papers.- A Hierarchical Model for Estimating Distribution Profiles of Soil Texture.- Assessing the Homogeneity of Three Odds Ratios: A Case Study in Small-sample Inference.- Modeling Rates of Bone Growth and Loss Using Order-Restricted Inference and Stochastic Change Points.- Estimating Genotype Probabilities in Complex Pedigrees.- Identifying Carriers of a Genetic Modifier Using Nonparametric Bayesian Methods.- Bayesian Tools for EDA and Model Building: A Brainy Study.- Bayesian Protein Structure Prediction.- Bayesian Analysis of Sensory Inputs of a Mobile Robot.- Hidden Markov Models for Analysis of Biological Rhythm Data.- Using Prior Opinions to Examine Sample Size in Two Clinical Trials.- Author Index.

The 5th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University campus on September 24-25, 1999. As in the past, the workshop featured both invited and contributed case studies. The former were presented and discussed in detail while the latter were presented in poster format. This volume contains the three invited case studies with the accompanying discussion as well as ten contributed pa pers selected by a refereeing process. The majority of case studies in the volume come from biomedical research. However, the reader will also find studies in education and public policy, environmental pollution, agricul ture, and robotics. INVITED PAPERS The three invited cases studies at the workshop discuss problems in ed ucational policy, clinical trials design, and environmental epidemiology, respectively. 1. In School Choice in NY City: A Bayesian Analysis ofan Imperfect Randomized Experiment J. Barnard, C. Frangakis, J. Hill, and D. Rubin report on the analysis of the data from a randomized study conducted to evaluate the New YorkSchool Choice Scholarship Pro gram. The focus ofthe paper is on Bayesian methods for addressing the analytic challenges posed by extensive non-compliance among study participants and substantial levels of missing data. 2. In Adaptive Bayesian Designs for Dose-Ranging Drug Trials D. Berry, P. Mueller, A. Grieve, M. Smith, T. Parke, R. Blazek, N.