Probability: Theory and Examples
by Rick Durrett
Publisher: Cambridge University Press 2010
Number of pages: 372
This book is an introduction to probability theory covering laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. It is a comprehensive treatment concentrating on the results that are the most useful for applications. Its philosophy is that the best way to learn probability is to see it in action.
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by Cosma Rohilla Shalizi - Carnegie Mellon University
Text for a second course in stochastic processes. It is assumed that you have had a first course on stochastic processes, using elementary probability theory. You will study stochastic processes within the framework of measure-theoretic probability.
by Edward Nelson - Princeton University Press
In this book Nelson develops a new approach to probability theory that is just as powerful as but much simpler than conventional Kolmogorov-style probability theory used throughout mathematics for most of the 20th century.
by David Nualart - The University of Kansas
From the table of contents: Stochastic Processes (Probability Spaces and Random Variables, Definitions and Examples); Jump Processes (The Poisson Process, Superposition of Poisson Processes); Markov Chains; Martingales; Stochastic Calculus.
by I. Todhunter - Kessinger Publishing, LLC
History of the probability theory from the time of Pascal to that of Laplace (1865). Todhunter gave a close account of the difficulties involved and the solutions offered by each investigator. His studies were thorough and fully documented.