E-Book, Englisch, 285 Seiten, eBook
Roe Probability and Statistics in the Physical Sciences
3rd Auflage 2020
ISBN: 978-3-030-53694-7
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, 285 Seiten, eBook
Reihe: Undergraduate Texts in Physics
ISBN: 978-3-030-53694-7
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Upper undergraduate
Autoren/Hrsg.
Weitere Infos & Material
1. 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 Matter




