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.