Probability for statistics and machine learning :
DasGupta, Anirban.
Probability for statistics and machine learning : fundamentals and advanced topics / Anirban DasGupta. - New York : Springer, c2011. - xix, 782 p. : ill. ; 24 cm. - Springer texts in statistics. .
Includes bibliographical references and indexes.
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. TOC
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...
1441996338 9781441996336 9781441996343 (ebk.)
2011924777
Probabilities.
Stochastic processes.
Mathematical statistics.
QA273 / .D275 2011
519.2 / DAP 2011
Probability for statistics and machine learning : fundamentals and advanced topics / Anirban DasGupta. - New York : Springer, c2011. - xix, 782 p. : ill. ; 24 cm. - Springer texts in statistics. .
Includes bibliographical references and indexes.
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. TOC
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...
1441996338 9781441996336 9781441996343 (ebk.)
2011924777
Probabilities.
Stochastic processes.
Mathematical statistics.
QA273 / .D275 2011
519.2 / DAP 2011