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Añadir al carritoCondición: New. pp. XIII, 285 54 illus., 5 illus. in color.
Idioma: Inglés
Publicado por Springer Nature Switzerland AG, CH, 2020
ISBN 10: 3030536939 ISBN 13: 9783030536930
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
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Añadir al carritoPaperback. Condición: New. Third Edition 2020. 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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Publicado por Springer International Publishing, 2020
ISBN 10: 3030536939 ISBN 13: 9783030536930
Librería: moluna, Greven, Alemania
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Añadir al carritoCondición: New. Features worked problems with solutions plus homework exercisesIncludes amusing, often-neglected applicationsAssumes no prior knowledge of statistical techniquesOffers an introduction to neural net techniques.
Idioma: Inglés
Publicado por Springer International Publishing, Springer Nature Switzerland, 2020
ISBN 10: 3030536939 ISBN 13: 9783030536930
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Añadir al carritoTaschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Idioma: Inglés
Publicado por Springer Nature Switzerland AG, CH, 2020
ISBN 10: 3030536939 ISBN 13: 9783030536930
Librería: Rarewaves.com UK, London, Reino Unido
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Añadir al carritoPaperback. Condición: New. Third Edition 2020. 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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Añadir al carritoPaperback. Condición: Brand New. 3rd edition. 300 pages. 9.25x6.10x0.71 inches. In Stock. This item is printed on demand.
Idioma: Inglés
Publicado por Springer International Publishing Sep 2020, 2020
ISBN 10: 3030536939 ISBN 13: 9783030536930
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 300 pp. Englisch.
Idioma: Inglés
Publicado por Springer, Springer Sep 2020, 2020
ISBN 10: 3030536939 ISBN 13: 9783030536930
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 69,54
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 300 pp. Englisch.