An Introduction to Probability Theory and Its Applications [2/2]

The fundamental character and spirit of this classic remain unchanged, but there are up-dated, revised, and new materials. This volume features typically thorough coverage--both applied and abstract--of the most "popular" densities, along with the measure-theocratic bases of the theory. Probabilistic subjects include the laws of large numbers, the central limit theorem, infinitely divisible distributions, Markov processes, random walks and renewal theory. Included, too, in the techniques are Laplace and Fourier transforms, semi-groups and general harmonic analysis. Experts will find new proofs and results, particularly the rewritten chapter 17. This edition consolidates and unifies the general methodology, obtaining coherence through the resultant simplification. --back cover