Probability and Statistics in the Physical Sciences (Undergraduate Texts in Physics) - Softcover

Roe, Byron P.

Softcover

ISBN 10: 3030536939 ISBN 13: 9783030536930

Publisher: Springer, 2020

Synopsis

This book, now in its third edition, offers a practical guide to the use of probability and statistics in experimental physics that is of value for both advanced undergraduates and graduate students. Focusing on applications and theorems and techniques actually used in experimental research, it includes worked problems with solutions, as well as homework exercises to aid understanding. Suitable for readers with no prior knowledge of statistical techniques, the book comprehensively discusses the topic and features a number of interesting and amusing applications that are often neglected. Providing an introduction to neural net techniques that encompasses deep learning, adversarial neural networks, and boosted decision trees, this new edition includes updated chapters with, for example, additions relating to generating and characteristic functions, Bayes’ theorem, the Feldman-Cousins method, Lagrange multipliers for constraints, estimation of likelihood ratios, and unfolding problems.

About this title

About the Author

Byron P. Roe is Professor Emeritus of Physics at the University of Michigan. He is a specialist in Experimental Nuclear and Subatomic Physics and Experimental Elementary Particle Physics. Professor Roe worked on an extensive number of experiments at Fermilab, CERN, and Argonne for more than 50 years and was often the spokesperson or co-spokesperson for these experiments. He has worked with the MiniBooNE neutrino experiment for almost 20 years. He is a Fellow of the American Physical Society.

From the Back Cover

This book, now in its third edition, offers a practical guide to the use of probability and statistics in experimental physics that is of value for both advanced undergraduates and graduate students. Focusing on applications and theorems and techniques actually used in experimental research, it includes worked problems with solutions, as well as homework exercises to aid understanding. Suitable for readers with no prior knowledge of statistical techniques, the book comprehensively discusses the topic and features a number of interesting and amusing applications that are often neglected. Providing an introduction to neural net techniques that encompasses deep learning, adversarial neural networks, and boosted decision trees, this new edition includes updated chapters with, for example, additions relating to generating and characteristic functions, Bayes’ theorem, the Feldman-Cousins method, Lagrange multipliers for constraints, estimation of likelihood ratios, and unfolding problems.

Edition details

• Publisher: Springer
• Publication date: 2020
• Language: English
• ISBN 10: 3030536939
• ISBN 13: 9783030536930
• Binding: Paperback
• Edition number: 3
• Number of pages: 298