Stochastic processes /

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Bibliographic Details
Author / Creator:Bass, Richard F.
Imprint:Cambridge ; New York : Cambridge University Press, 2011.
Description:1 online resource (xv, 390 pages) : illustrations
Language:English
Series:Cambridge series in statistical and probabilistic mathematics ; 33
Cambridge series on statistical and probabilistic mathematics ; 33.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11830526
Hidden Bibliographic Details
ISBN:9781139128452
1139128450
1139115626
9781139115629
9780511997044
0511997043
9781139117791
1139117793
9781107008007
110700800X
9781139115629
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1107221870
9781107221871
1283315009
9781283315005
9786613315007
6613315001
1139123548
9781139123549
1139113437
9781139113434
Notes:Includes bibliographical references and index.
English.
Print version record.
Summary:"This comprehensive guide to stochastic processes gives a complete overview of the theory and addresses the most important applications. Pitched at a level accessible to beginning graduate students and researchers from applied disciplines, it is both a course book and a rich resource for individual readers. Subjects covered include Brownian motion, stochastic calculus, stochastic differential equations, Markov processes, weak convergence of processes and semigroup theory. Applications include the Black-Scholes formula for the pricing of derivatives in financial mathematics, the Kalman-Bucy filter used in the US space program and also theoretical applications to partial differential equations and analysis. Short, readable chapters aim for clarity rather than full generality. More than 350 exercises are included to help readers put their new-found knowledge to the test and to prepare them for tackling the research literature"--
"In a first course on probability one typically works with a sequence of random variables X1,X2 ... For stochastic processes, instead of indexing the random variables by the non-negative integers, we index them by t G [0, oo) and we think of Xt as being the value at time t. The random variable could be the location of a particle on the real line, the strength of a signal, the price of a stock, and many other possibilities as well. We will also work with increasing families of s -fields {J-t}, known as filtrations. The s -field J-t is supposed to represent what we know up to time t. 1.1 Processes and s -fields Let (Q., J-, P) be a probability space. A real-valued stochastic process (or simply a process) is a map X from [0, oo) x Q. to the reals. We write Xt = Xt = X(t,?). We will impose stronger measurability conditions shortly, but for now we require that the random variables Xt be measurable with respect to J- for each t 0. A collection of s -fields J-t such that J-t C J- for each t and J-s C J-t if s t is called a filtration. Define J-t+ = P\e0J-t+e. A filtration is right continuous if J-t+ = J-t for all t 0"--
Other form:Print version: Bass, Richard F. Stochastic processes. Cambridge ; New York : Cambridge University Press, 2011 9781107008007
Standard no.:9786613315007