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Probability for statistics and machine learning : fundamentals and advanced topics / Anirban DasGupta.

By: DasGupta, Anirban.
Material type: TextTextSeries: 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
Contents:
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.
Summary: Summary: 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...
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Item type Current location Collection Call number Copy number Status Date due Barcode Item holds
E-Book E-Book EWU Library
E-book
Non-fiction 519.2 DAP 2011 (Browse shelf) Not for loan
Text 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.

Summary:
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...

Applied Statistics

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