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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.
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.
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."
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