Probability and Statistics in the Physical Sciences
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Beschreibung
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.
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.
Features worked problems with solutions plus homework exercises Includes amusing, often-neglected applications Assumes no prior knowledge of statistical techniques Offers an introduction to neural net techniques Request lecturer material: sn.pub/lecturer-material
Autor*in
Byron P. Roe
Themen in »Probability and Statistics in the Physical Sciences«
Statistical issues in experimental physics Experimental particle physics Experimental neutrino physics Statistical issues for experimental scientists Generating functions Characteristic functions Monte Carlo method Queuing theory Central limit theorem Bayes theorem Estimation of likelihood ratios Unfolding problems Neural net techniques data-driven science, modeling and theory building
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“The depth and the manner in which the material is treated will make it easy for the students to transition to more advanced topics such as deep learning, machine learning and artificial intelligence after perusing the book. … Roe’s book is a wonderful, forward looking introduction to probability and statistics and its applications. Hence, I have no hesitations whatsoever in recommending the book – both to the students and instructors.” (Mogadalai P Gururajan, Contemporary Physics, August 19, 2021)
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