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Image of - Probability And Statistics In The Physical Sciences Third Edition | Paperback
Probability And Statistics In The Physical Sciences Third Edition | Paperback

Probability And Statistics In The Physical Sciences Third Edition | Paperback

by Roe B.P.

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.

Highlights

  • binding-icon

    9783030536930

    ISBN:

  • binding-icon

    Roe B.P.

    Author:

  • binding-icon

    285

    Pages:

  • binding-icon

    239 gm

    Weight:

  • langauage-icon

    English

    Language:

  • date-icon

    2020

    Year:

  • edition-icon

    Third Edition

    Edition:

  • binding-icon

    Paperback

    Binding:

5254

6567

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.

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