Author: Publisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
Invited Contribution to the Discussion of R.E. Kass: the Geometry of Asymptotic Inference
Author: Invited contribution to the discussion of R.E. Kass: "The geometry of asymptotic inference"
Author: O. E. Barndorff-NielsenPublisher:
ISBN:
Category :
Languages : da
Pages : 20
Book Description
Research Reports
Author: Invited Contribution to the Discussion of R.E. Kass
Author: Ole Eiler Barndorff-NielsenPublisher:
ISBN:
Category :
Languages : en
Pages : 20
Book Description
The Geometry of Asymptotic Inference
Author: Robert E. KassAnalysis of Neural Data
Author: Robert E. KassPublisher: Springer
ISBN: 1461496020
Category : Medical
Languages : en
Pages : 663
Book Description
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.
Annales de la faculté des sciences de Toulouse
Author: Principles of Statistical Inference
Author: D. R. CoxPublisher: Cambridge University Press
ISBN: 1139459139
Category : Mathematics
Languages : en
Pages : 227
Book Description
In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years. Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field. An appendix gives a more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications across the sciences and associated technologies. The mathematics is kept as elementary as feasible, though previous knowledge of statistics is assumed. The book will be valued by every user or student of statistics who is serious about understanding the uncertainty inherent in conclusions from statistical analyses.
Tensor Methods in Statistics
Author: Peter McCullaghPublisher: Courier Dover Publications
ISBN: 0486832694
Category : Mathematics
Languages : en
Pages : 308
Book Description
A pioneering monograph on tensor methods applied to distributional problems arising in statistics, this work begins with the study of multivariate moments and cumulants. An invaluable reference for graduate students and professional statisticians. 1987 edition.
Bayesian Data Analysis, Third Edition
Author: Andrew GelmanPublisher: CRC Press
ISBN: 1439840954
Category : Mathematics
Languages : en
Pages : 677
Book Description
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.