Probability and statistics in the physical sciences
Material type: TextPublication details: Switzerland Springer 2020Edition: 3Description: 285 pISBN: 9783030536930Subject(s): Mathematical physics | Probabilities | Statistical physics | Nuclear physics | physics | statistics | EconophysicsDDC classification: 530.1595 Summary: 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.Item type | Current library | Collection | Call number | Status | Date due | Barcode |
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BK | Stack | Stack | 530.1595 ROE/P (Browse shelf (Opens below)) | Available | 59567 |
Browsing Kannur University Central Library shelves, Shelving location: Stack, Collection: Stack Close shelf browser (Hides shelf browser)
530.1563 FLE/V A Student's Guide to Vectors and Tensors | 530.15635 LAU/M Mathematical models in science | 530.159 5 HUA/I Introduction to statistical physics / | 530.1595 ROE/P Probability and statistics in the physical sciences | 530.3 WAL/I Introduction to statistical methods | 530.41 ALT/C Condensedmatter field theory | 530.41 BAS/S Solid state physics |
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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