Probability for statistics and machine learning : fundamentals and advanced topics / Anirban DasGupta.
Material type:
Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds |
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Dr. S. R. Lasker Library, EWU E-book | Non-fiction | 519.2 DAP 2011 (Browse shelf(Opens below)) | Not for loan | ||||
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Dr. S. R. Lasker Library, EWU Reserve Section | Non-fiction | 519.2 DAP 2011 (Browse shelf(Opens below)) | C-1 | Not For Loan | 26617 |
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...
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Saifun Momota
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