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

By: DasGupta, AnirbanMaterial type: TextTextLanguage: English Series: Springer texts in statisticsPublication details: New York : Springer, c2011. Description: xix, 782 p. : ill. ; 24 cmISBN: 1441996338; 9781441996336; 9781441996343 (ebk.)Subject(s): Probabilities | Stochastic processes | Mathematical statisticsDDC classification: 519.2 LOC classification: QA273 | .D275 2011Online resources: WorldCat details | E-book Fulltext
Contents:
TOC 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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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode Item holds
E-Book E-Book Dr. S. R. Lasker Library, EWU
E-book
Non-fiction 519.2 DAP 2011 (Browse shelf(Opens below)) Not for loan
Text Text Dr. S. R. Lasker Library, EWU
Reserve Section
Non-fiction 519.2 DAP 2011 (Browse shelf(Opens below)) C-1 Not For Loan 26617
Total holds: 0

Includes bibliographical references and indexes.

TOC 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...

AS

Saifun Momota

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