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 -