In the past decade, major progress has been made in understanding mec- nisms of arrhythmias. This progress stems from much-improved experim- tal, genetic, and computational techniques that have helped to clarify the roles of speci?c proteins in the cardiac cycle, including ion channels, pumps, - changer, adaptor proteins, cell-surface receptors, and contractile proteins. The interactions of these components, and their individual potential as therap- tic targets, have also been studied in detail, via an array of new imaging and sophisticated experimental modalities. The past 10 years have also led to the realization that genetics plays a predominant role in the development of lethal arrhythmias. Many of the topics discussed in this text re?ect very recently undertaken research directions including the genetics of arrhythmias, cell signaling mo- cules as potential therapeutic targets, and traf?cking to the membrane. These new approaches and implementations of anti-arrhythmic therapy derive from many decades of research as outlined in the ?rst chapter by the distinguished professors Michael Rosen (Columbia University) and Michiel Janse (University of Amsterdam). The text covers changes in approaches to arrhythmia therapy over time, in multiple cardiac regions, and over many scales, from gene to protein to cell to tissue to organ.

Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

The 6th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University in October, 2001. This volume contains the invited case studies with the accompanying discussion as well as contributed papers selected by a refereeing process.

The series of workshops Case Studies in Bayesian Statistics at Carnegie Mellon University is unique in devoting an entire meeting to extended presentation and discussion of scientific investigations in which statisticians play central roles within integrated, cross- disciplinary teams. The goal has been to elucidate the interplay between Bayesian theory and practice, and thereby identify successful methods and indicate important directions for future research. This volume contains the four invited case studies, with accompanying discussion, and nine contributed papers, from the 4th workshop, which was held September 27-28, 1997. While most of the case studies in this volume come from biomedical research, the reader will also find studies in environmental science and marketing research. Students and teachers of statistics, research statisticians, and investigators from other fields should find a wealth of ideas and methods in this series of case studies.

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."

This third volume of case studies presents detailed applications of Bayesian statistical analysis, emphasising the scientific context. The papers were presented and discussed at a workshop held at Carnegie-Mellon University, and this volume - dedicated to the memory of Morrie Groot-reproduces six invited papers, each with accompanying invited discussion, and nine contributed papers with the focus on econometric applications.

The past few years have witnessed dramatic advances in computational methods for Bayesian inference. As a result, Bayesian approaches to solving a wide variety of problems in data analysis and decision-making have become feasible, and there is currently a growth spurt in the application of Bayesian methods. The purpose of this volume is to present several detailed examples of applications of Bayesian thinking, with an emphasis on the scientific or technological context of the problem being solved. The papers collected here were presented and discussed at a Workshop held at Carnegie-Mellon University, September 29 through October 1, 1991. There are five ma jor articles, each with two discussion pieces and a reply. These articles were invited by us following a public solicitation of abstracts. The problems they address are diverse, but all bear on policy decision-making. Though not part of our original design for the Workshop, that commonality of theme does emphasize the usefulness of Bayesian meth ods in this arena. Along with the invited papers were several additional commentaries of a general nature; the first comment was invited and the remainder grew out of the discussion at the Workshop. In addition there are nine contributed papers, selected from the thirty-four presented at the Workshop, on a variety of applications. This collection of case studies illustrates the ways in which Bayesian methods are being incorporated into statistical practice. The strengths (and limitations) of the approach become apparent through the examples."