Introduction to Statistics and Data Analysis for Physicists

Introduction to Statistics and Data Analysis for Physicists

A free statistic textbook which focuses on modern applications in nuclear and particle physics. Topics include data analysis in modern experiments and likelihood.

Tag(s): Statistics

Publication date: 01 Feb 2010

ISBN-10: n/a

ISBN-13: 978393570241

Paperback: 412 pages

Views: 1,511

Type: Textbook

Publisher: Deutsches Elektronen-Synchrotron

License: n/a

Post time: 22 Oct 2016 08:00:00

Introduction to Statistics and Data Analysis for Physicists

Introduction to Statistics and Data Analysis for Physicists A free statistic textbook which focuses on modern applications in nuclear and particle physics. Topics include data analysis in modern experiments and likelihood.
Tag(s): Statistics
Publication date: 01 Feb 2010
ISBN-10: n/a
ISBN-13: 978393570241
Paperback: 412 pages
Views: 1,511
Document Type: Textbook
Publisher: Deutsches Elektronen-Synchrotron
License: n/a
Post time: 22 Oct 2016 08:00:00
From the Preface:
Bohm and Zech wrote:There is a large number of excellent statistic books. Nevertheless, we think that it is justified to complement them by another textbook with the focus on modern applications in nuclear and particle physics. To this end we have included a large number of related examples and figures in the text. We emphasize less the mathematical foundations but appeal to the intuition of the reader. 

Data analysis in modern experiments is unthinkable without simulation techniques. We discuss in some detail how to apply Monte Carlo simulation to parameter estimation, deconvolution, goodness-of-fit tests. We sketch also modern developments like artificial neural nets, bootstrap methods, boosted decision trees and support vector machines. 

Likelihood is a central concept of statistical analysis and its foundation is the likelihood principle. We discuss this concept in more detail than usually done in textbooks and base the treatment of inference problems as far as possible on the likelihood function only, as is common in the majority of the nuclear and particle physics community. In this way point and interval estimation, error propagation, combining results, inference of discrete and continuous parameters are consistently treated. We apply Bayesian methods where the likelihood function is not sufficient to proceed to sensible results, for instance in handling systematic errors, deconvolution problems and in some cases when nuisance parameters have to be eliminated, but we avoid improper prior densities. Goodness-of-fit and significance tests, where no likelihood function exists, are based on standard frequentist methods.




About The Author(s)


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Gerhard Bohm

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No information is available for this author.

Günter Zech

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