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Probability for statistics and machine learning : fundamentals and advanced topics

Author: Anirban DasGupta
Publisher: New York : Springer, ©2011.
Series: Springer texts in statistics.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

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  Read more...

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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Anirban DasGupta
ISBN: 1441996338 9781441996336 9781441996343 1441996346
OCLC Number: 706920643
Description: xix, 782 pages : illustrations ; 24 cm.
Contents: Review of univariate probability --
Multivariate discrete distributions --
Multidimensional densities --
Advanced distribution theory --
Multivariate normal and related distributions --
Finite sample theory of order statistics and extremes --
Essential asymptotics and applications --
Characteristics functions and applications --
Asymptotoics of extremes and order statistics --
Markov chains and application --
Random walks --
Brownian motion and Gaussian processes --
Poisson processes and applications --
Discrete time martingales and concentration inequalities --
Probability metrics --
Empirical processes and VC theory --
Large deviations --
The exponential family and statistical applications --
Simulation and Markov chain Monte Carlo --
Useful tools for statistics and machine learning.
Series Title: Springer texts in statistics.
Responsibility: Anirban DasGupta.
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From the reviews:"It is a companion second volume to the author's undergraduate text Fundamentals of Probability: A First course ... . The author seeks to provide readers with a comprehensive Read more...

 
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