000			01823 a2200193 4500
005			20251114155429.0
008			241224b |||||||| |||| 00| 0 eng d
020			_a9781461428848
041			_aeng
082			_a519.2 _bDasP
100			_aDasGupta, Anirban
245			_aProbability for Statistics and Machine Learning : _bFundamentals and advanced topics / _cAnirban DasGupta
260			_aNew York : _bSpringer, _c©2011.
300			_axix, 782 p
520			_aThis book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance. This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability.
650			_aProbabilities
650			_aMathematical models
942			_cBK
999			_c7234 _d7234