000			01961 a2200193 4500
005			20251114142439.0
008			250915b |||||||| |||| 00| 0 eng d
020			_a9783031199332
082			_a519.5 _bLisS3
100			_a Lista, Luca
245			_aStatistical Methods for Data Analysis: _bWith Applications in Particle Physics [3rd Ed.] / _c Lista, Luca.
250			_a3rd ed.
260			_aItaly: _bSpringer, _c©2024.
300			_axxx, 334p.
440			_aLecture Notes in Physics _vv. 1010
520			_aThis third edition expands on the original material. Large portions of the text have been reviewed and clarified. More emphasis is devoted to machine learning including more modern concepts and examples. This book provides the reader with the main concepts and tools needed to perform statistical analyses of experimental data, in particular in the field of high-energy physics (HEP). It starts with an introduction to probability theory and basic statistics, mainly intended as a refresher from readers’ advanced undergraduate studies, but also to help them clearly distinguish between the Frequentist and Bayesian approaches and interpretations in subsequent applications. Following, the author discusses Monte Carlo methods with emphasis on techniques like Markov Chain Monte Carlo, and the combination of measurements, introducing the best linear unbiased estimator. More advanced concepts and applications are gradually presented, including unfolding and regularization procedures, culminating in the chapter devoted to discoveries and upper limits. The reader learns through many applications in HEP where the hypothesis testing plays a major role and calculations of look-elsewhere effect are also presented. Many worked-out examples help newcomers to the field and graduate students alike understand the pitfalls involved in applying theoretical concepts to actual data.
650			_aStatistical methods _xProbability
942			_cBK
999			_c7363 _d7363