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Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics (Springer Texts in Statistics)

Amazon.com Price:  $78.93 (as of 22/04/2019 22:21 PST- Details)

Description

This book provides a versatile and lucid remedy of classic in addition to up to date probability theory, at the same time as integrating them with core topics in statistical theory and in addition some key tools in machine learning. It’s written in an extremely accessible style, with elaborate motivating discussions and a large number of worked out examples and exercises. The book has 20 chapters on a variety of topics, 423 worked out examples, and 808 exercises. It’s unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive remedy of many topics of current importance.

This book can be utilized 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 set of rules, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability.

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