Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics eBook

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Probability for Statistics and Machine Learning
Fundamentals and Advanced Topics
This 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.

This is a digital product.

Additional ISBNs
9781461428848

Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics is written by Anirban DasGupta and published by Springer. The Digital and eTextbook ISBNs for Probability for Statistics and Machine Learning are 9781441996343, 1441996346 and the print ISBNs are 9781441996336, 1441996338. Additional ISBNs for this eTextbook include 9781461428848.