Description
Book SynopsisThis 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.
Trade Review“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)
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 Matter
