Probability for statistics and machine learning
fundamentals and advanced topics
DasGupta, Anirban.
creator
text
bibliography
nyu
New York
Springer
c2011
2011
monographic
eng
xix, 782 p. : ill. ; 24 cm.
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...
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.
general
Anirban DasGupta.
Includes bibliographical references and indexes.
AS
Probabilities
Stochastic processes
Mathematical statistics
QA273 .D275 2011
519.2 DAP 2011
Springer texts in statistics
1441996338
9781441996336
9781441996343 (ebk.)
2011924777
http://www.worldcat.org/title/probability-for-statistics-and-machine-learning-fundamentals-and-advanced-topics/oclc/706920643&referer=brief_results
http://lib.ewubd.edu/ebook/4527
http://www.worldcat.org/title/probability-for-statistics-and-machine-learning-fundamentals-and-advanced-topics/oclc/706920643&referer=brief_results
http://lib.ewubd.edu/ebook/4527
YDXCP
110309
20180107150853.0
4527
eng