Description: Probability for Statistics and Machine Learning : Fundamentals and Advanced Topics, Hardcover by Dasgupta, Anirban, ISBN 1441996338, ISBN-13 9781441996336, Like New Used, Free P&P in the UK
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. Th 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.
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Book Title: Probability for Statistics and Machine Learning : Fundamentals an
Number of Pages: 784 Pages
Language: English
Publication Name: Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics
Publisher: Springer-Verlag New York Inc.
Publication Year: 2011
Subject: Engineering & Technology, Computer Science, Biology, Mathematics
Item Height: 235 mm
Item Weight: 1364 g
Type: Textbook
Author: Anirban Dasgupta
Series: Springer Texts in Statistics
Item Width: 155 mm
Format: Hardcover
