Probability for statistics and machine learning : fundamentals and advanced topics / Anirban DasGupta.
By: DasGupta, Anirban.Material type: TextSeries: Springer texts in statistics.Publisher: New York : Springer, c2011Description: xix, 782 p. : ill. ; 24 cm.ISBN: 1441996338; 9781441996336; 9781441996343 (ebk.).Subject(s): Probabilities | Stochastic processes | Mathematical statisticsDDC classification: 519.2 Online resources: WorldCat details | E-book Fulltext
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|E-Book||EWU Library E-book||Non-fiction||519.2 DAP 2011 (Browse shelf)||Not for loan|
|Text||EWU Library Reserve Section||Non-fiction||519.2 DAP 2011 (Browse shelf)||C-1||Not For Loan||26617|
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Includes bibliographical references and indexes.
Table of 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.
This accessible book provides a versatile 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...