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OverviewThis 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. Full Product DetailsAuthor: Byron P. RoePublisher: Springer Nature Switzerland AG Imprint: Springer Nature Switzerland AG Edition: 3rd ed. 2020 Weight: 0.462kg ISBN: 9783030536930ISBN 10: 3030536939 Pages: 285 Publication Date: 27 September 2020 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of Contents1. Front Matter Pages i-xi 2. Basic Probability Concepts 3. Some Initial Definitions 4. Some Results Independent of Specific Distributions 5. Discrete Distributions and Combinatorials 6. Specific Discrete Distributions 7. The Normal (or Gaussian) Distribution and Other Continuous Distributions 8. Generating Functions and Characteristic Functions 9. The Monte Carlo Method: Computer Simulation of Experiments 10. Queueing Theory and Other Probability Questions 11. Two-Dimensional and Multidimensional Distributions 12. The Central Limit Theorem 13. Inverse Probability; Confidence Limits 14. Methods for Estimating Parameters. Least Squares and Maximum Likelihood 15. Curve Fitting 16. Bartlett S Function; Estimating Likelihood Ratios Needed for an Experiment 17. Interpolating Functions and Unfolding Problems 18. Fitting Data with Correlations and Constraints 19. Beyond Maximum Likelihood and Least Squares; Robust Methods 20. Back MatterReviewsThe 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) “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) Author InformationByron 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. Tab Content 6Author Website:Countries AvailableAll regions |
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