000			02389nam a2200433 4500
003			EC-UrYT
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008			150116t9999 uk r | 000 0 eng d
020			_a9783527410866
040			_aEC-UrYT _cEC-UrYT
041			_aeng
082	0	4	_223 _a539.72015195
100	1		_97589 _aNarsky, Ilya
245	1	0	_aStatistical analysis techniques in particle physics : _bfits, density estimation and supervised learning / _cIlya Narsky, Frank C. Porter
250			_aFirst edition
264	3	4	_aWeinheim : _bWiley-VCH, _c2014.
300			_a441 pages : _billustrations, figures ; _c24 cm
505	2		_aWhy We Wrote This Book and How You Should Read It -- Parametric Likelihood Fits -- Goodness of Fit -- Resampling Techniques -- Density Estimation -- Basic Concepts and Definitions of Machine Learning -- Data Preprocessing -- Linear Transformations and Dimensionality Reduction -- Introduction to Classification -- Assessing Classifier Performance -- Linear and Quadratic Discriminant Analysis, Logistic Regression, and Partial Least Squares Regression -- Neural Networks -- Local Learning and Kernel Expansion -- Decision Trees -- Ensemble Learning -- Reducing Multiclass to Binary -- How to Choose the Right Classifier for Your Analysis and Apply It Correctly -- Methods for Variable Ranking and Selection -- Bump Hunting in Multivariate Data -- Software Packages for Machine Learning.
520	3		_aModern analysis of HEP data needs advanced statistical tools to separate signal from background. This is the first book which focuses on machine learning techniques. It will be of interest to almost every high energy physicist, and, due to its coverage, suitable for students.
650	2	4	_95409 _aParticles (Nuclear physics) _xStatistical methods.
650	2	4	_95411 _aPartículas (Física nuclear) _xMétodos estadísticos
650	2	4	_9432 _aPhysics
650	2	4	_9102 _aFísica
650	2	4	_95708 _aCondensed matter
650	2	4	_95709 _aMateria Condensada
700	1		_aPorter, Frank C. _eauthor _q(Frank Clifford) _97590
942			_2ddc _cLIBRO
999			_c3219