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.- Modeling Customer Survey Data.- Functional Connectivity in the Cortical Circuits Subserving Eye Movements.- Modeling Risk of Breast Cancer and Decisions about Genetic Testing.- The Bayesian Approach to Population Pharmacokinetic/pharmacodynamic Modeling.- Contributed Papers.- Longitudinal Modeling of the Side Effects of Radiation Therapy.- Analysis of Hospital Quality Monitors using Hierarchical Time Series Models.- Spatio-Temporal Hierarchical Models for Analyzing Atlanta Pediatric Asthma ER Visit Rates.- Validating Bayesian Prediction Models: a Case Study in Genetic Susceptibility to Breast Cancer.- Mixture Models in the Exploration of Structure-Activity Relationships in Drug Design.- Population Models for Hematologic Data.- A Hierarchical Spatial Model for Constructing Wind Fields from Scatterometer Data in the Labrador Sea.- Redesigning a Network of Rainfall Stations.- Using PSA to Detect Prostate Cancer Onset: An Application of Bayesian Retrospective and Prospective Changepoint Identification.- Author Index.

The 4th Workshop on Case Studies in Bayesian Statistics was held at the Car negie Mellon University campus on September 27-28, 1997. 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 four invited case studies with the accompanying discus sion as well as nine contributed papers selected by a refereeing process. While most of the case studies in the volume come from biomedical research the reader will also find studies in environmental science and marketing research. INVITED PAPERS In Modeling Customer Survey Data, Linda A. Clark, William S. Cleveland, Lorraine Denby, and Chuanhai LiD use hierarchical modeling with time series components in for customer value analysis (CVA) data from Lucent Technologies. The data were derived from surveys of customers of the company and its competi tors, designed to assess relative performance on a spectrum of issues including product and service quality and pricing. The model provides a full description of the CVA data, with random location and scale effects for survey respondents and longitudinal company effects for each attribute. In addition to assessing the performance of specific companies, the model allows the empirical exploration of the conceptual basis of consumer value analysis. The authors place special em phasis on graphical displays for this complex, multivariate set of data and include a wealth of such plots in the paper.