This graduate-level text offers a comprehensive account of the general theory of stationary processes, with special emphasis on the properties of sample functions. Assuming a familiarity with the basic features of modern probability theory, the text develops the foundations of the general theory of s... read more
Lectures on the Coupling Method by Torgny Lindvall Practical and easy-to-use reference progresses from simple to advanced topics, covering, among other topics, renewal theory, Markov chains, Poisson approximation, ergodicity, and Strassen's theorem. 1992 edition.
Elements of the Theory of Markov Processes and Their Applications by A. T. Bharucha-Reid Graduate-level text and reference in probability, with numerous scientific applications. Nonmeasure-theoretic introduction to theory of Markov processes and to mathematical models based on the theory. Appendixes. Bibliographies. 1960 edition.
Nonstandard Methods in Stochastic Analysis and Mathematical Physics by Sergio Albeverio, Jens Erik Fenstad, Raphael Høegh-Krohn, Tom Lindstrøm Two-part treatment begins with a self-contained introduction to the subject, followed by applications to stochastic analysis and mathematical physics. "A welcome addition." — Bulletin of the American Mathematical Society. 1986 edition.
Stochastic Differential Equations and Applications by Avner Friedman Originally published in 2 volumes, this text develops the theory of systems of stochastic differential equations and presents applications in probability, partial differential equations, and stochastic control problems. 1975 edition.
Introduction to Probability by John E. Freund Featured topics include permutations and factorials, probabilities and odds, frequency interpretation, mathematical expectation, decision making, postulates of probability, rule of elimination, much more. Exercises with some solutions. Summary. 1973 edition.
This graduate-level text offers a comprehensive account of the general theory of stationary processes, with special emphasis on the properties of sample functions. Assuming a familiarity with the basic features of modern probability theory, the text develops the foundations of the general theory of stochastic processes, examines processes with a continuous-time parameter, and applies the general theory to procedures key to the study of stationary processes. Additional topics include analytic properties of the sample functions and the problem of time distribution of the intersections between a sample function. 1967 edition.
Unabridged republication of the edition published by John Wiley & Sons, Inc., New York, 1967.
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