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Statistical methods for data analysiswith applications in particle physics /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical methods for data analysisby Luca Lista.
其他題名:	with applications in particle physics /
作者:	Lista, Luca.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	xxx, 334 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Particles (Nuclear physics)Statistical methods.
電子資源:	https://doi.org/10.1007/978-3-031-19934-9
ISBN:	9783031199349$q(electronic bk.)

Statistical methods for data analysiswith applications in particle physics /
Lista, Luca.

Statistical methods for data analysiswith applications in particle physics /[electronic resource] :by Luca Lista. - Third edition. - Cham :Springer International Publishing :2023. - xxx, 334 p. :ill., digital ;24 cm. - Lecture notes in physics,v. 10101616-6361 ;. - Lecture notes in physics ;650..

Introduction to Probability and Inference -- Discrete Probability Distributions -- Probability Density Functions -- Random Numbers and Monte Carlo Methods -- Bayesian Probability and Inference -- Frequentist Probability and Inference -- Combining Measurements -- Confidence Intervals -- Convolution and Unfolding -- Hypothesis Testing -- Machine Learning -- Discoveries and Limits.

This third edition expands on the original material. Large portions of the text have been reviewed and clarified. More emphasis is devoted to machine learning including more modern concepts and examples. This book provides the reader with the main concepts and tools needed to perform statistical analyses of experimental data, in particular in the field of high-energy physics (HEP) It starts with an introduction to probability theory and basic statistics, mainly intended as a refresher from readers' advanced undergraduate studies, but also to help them clearly distinguish between the Frequentist and Bayesian approaches and interpretations in subsequent applications. Following, the author discusses Monte Carlo methods with emphasis on techniques like Markov Chain Monte Carlo, and the combination of measurements, introducing the best linear unbiased estimator. More advanced concepts and applications are gradually presented, including unfolding and regularization procedures, culminating in the chapter devoted to discoveries and upper limits. The reader learns through many applications in HEP where the hypothesis testing plays a major role and calculations of look-elsewhere effect are also presented. Many worked-out examples help newcomers to the field and graduate students alike understand the pitfalls involved in applying theoretical concepts to actual data.

ISBN: 9783031199349$q(electronic bk.)

Standard No.: 10.1007/978-3-031-19934-9doiSubjects--Topical Terms:

754873
Particles (Nuclear physics)
--Statistical methods.

LC Class. No.: QC793.4 / .L57 2023

Dewey Class. No.: 539.72015195

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