TY  - BOOK
AU  - DasGupta,Anirban
TI  - Probability for statistics and machine learning: fundamentals and advanced topics 
T2  - Springer texts in statistics
SN  - 1441996338
AV  - QA273 .D275 2011
U1  - 519.2 23
PY  - 2011///
CY  - New York
PB  - Springer
KW  - Probabilities
KW  - Stochastic processes
KW  - Mathematical statistics
N1  - 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; AS
N2  - 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

UR  - http://www.worldcat.org/title/probability-for-statistics-and-machine-learning-fundamentals-and-advanced-topics/oclc/706920643&referer=brief_results
UR  - http://lib.ewubd.edu/ebook/4527
ER  -