Introduction To Statistics And Data Analysis For Physicists

Introduction to statistics and data analysis for physicists

Introduction to Statistics and Data Analysis for Physicists

"The tools of statistical analysis continue to prove indispensable for experiments. In modern physical applications, increasingly sophisticated and specific tools are needed to reliably extract results from complex data. This textbook thus presents a comprehensive treatment of the topic for the practicing physicist, focusing less on mathematical foundations but appealing to intuitive techniques. This fourth edition is greatly expanded with new sub-topics and examples. We begin with fundamental probability concepts and measurement errors, continuing to the crucial Monte Carlo simulation. The sections on parameter inference with constrained parameters, treatment of distorted data, and statistics of weighted events, among others, are updated with new developments. Likelihood and its underlying likelihood principle are explored with great detail, while final chapters expound on other advanced techniques such as statistical learning and hypothesis testing. Developed and greatly expanded from a popular graduate course at the University of Siegen, this book serves as an essential resource for all graduate students and researchers in physics seeking a rigorous foundation in statistical methods for experimental physics, especially those in nuclear and particle physics"-- Provided by publisher.

Introduction to Statistics and Data Analysis for Physicists (Fourth Edition)

The tools of statistical analysis for experiments in modern physical applications are increasingly sophisticated and specific tools are needed to reliably extract results from complex data. This textbook thus presents a comprehensive treatment of the topic for the practicing physicist, focusing less on mathematical foundations but appealing to intuitive techniques with a large number of examples.This fourth edition is greatly expanded with new sub-topics not covered in standard textbooks. We begin with fundamental probability concepts and measurement errors, continuing to the indispensable Monte Carlo simulation. Likelihood and its underlying likelihood principle are explored, serving as bases for the sections on parameter inference and the treatment of distorted data. Topics like hypothesis testing, the statistics of weighted events, the elimination of nuisance parameters, and deconvolution are updated with new developments. Final chapters introduce other advanced techniques such as statistical learning and bootstrap sampling.Developed and greatly expanded from a graduate course at the University of Siegen, this book serves as an essential resource for all graduate students and researchers seeking a rigorous foundation in statistical methods for experimental physics, especially those in nuclear, particle and astrophysics.