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Zusatztext "The material presented in this book is dense.In less than two hundred pages! it takes the reader from the basic notions of probability! through neural networks! Monte Carlo methods! and regularization techniques." Short Book Reviews Informationen zum Autor Dr Glen D. Cowan, CERN, PPE Division, CH-1211 Geneva 23, Switzerland, Tel: +41 22 767 6539, Fax: +41 22 767 9425, Email: glen.cowan@cern.chGlen Cowan received his Ph.D. in physics in 1988 from the University of California, Berkeley, after completing his thesis work on particle production in high energy electron-positron collisions. From there he moved to Europe, and has held positions at the Max Planck Institute in Munich and the University of Siegen, Germany. For most of this time, he has been based at the European Organisation for Nuclear Research (CERN) in Geneva. He is a member of the ALEPH experiment at the electron positron collider LEP. Klappentext This book is a guide to the practical application of statistics to data analysis in the physical sciences. It is primarily addressed at students and professionals who need to draw quantitative conclusions from experimental data. Although most of the examples are taken from particle physics! the material is presented in a sufficiently general way as to be useful to people from most branches of the physical sciences. The first part of the book describes the basic tools of data analysis: concepts of probability and random variables! Monte Carlo techniques! statistical tests! and methods of parameter estimation. The last three chapters then develop more advanced statistical ideas! focusing on interval estimation! characteristic functions! and correcting distributions for the effects of measurement errors (unfolding). Zusammenfassung The book is a guide to the practical application of statistics in data analysis as typically encountered in the physical sciences. It is primarily addressed at students and researchers who work with experimental data. The topics include probability, random variables, Monte Carlo techniques, statistical tests, parameter estimation, and unfolding methods. Inhaltsverzeichnis Preface Notation 1: Fundamental Concepts 2: Examples of Probability Functions 3: The Monte Carlo Method 4: Statistical Tests 5: General Concepts of Parameter Estimation 6: The Method of Maximum Likelihood 7: The Method of Least Squares 8: The Method of Moments 9: Statistical Errors, Confidence Intervals and Limits 10: Characteristic Functions and Related Examples 11: Unfolding Bibliography Index ...
