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Introduction to Infinite Dimensional Stochastic Analysis (Paperback, Softcover reprint of the original 1st ed. 2000)
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Introduction to Infinite Dimensional Stochastic Analysis (Paperback, Softcover reprint of the original 1st ed. 2000)
Series: Mathematics and Its Applications, 502
Expected to ship within 10 - 15 working days
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The infinite dimensional analysis as a branch of mathematical
sciences was formed in the late 19th and early 20th centuries.
Motivated by problems in mathematical physics, the first steps in
this field were taken by V. Volterra, R. GateallX, P. Levy and M.
Frechet, among others (see the preface to Levy 2]). Nevertheless,
the most fruitful direction in this field is the infinite
dimensional integration theory initiated by N. Wiener and A. N.
Kolmogorov which is closely related to the developments of the
theory of stochastic processes. It was Wiener who constructed for
the first time in 1923 a probability measure on the space of all
continuous functions (i. e. the Wiener measure) which provided an
ideal math ematical model for Brownian motion. Then some important
properties of Wiener integrals, especially the quasi-invariance of
Gaussian measures, were discovered by R. Cameron and W. Martin l,
2, 3]. In 1931, Kolmogorov l] deduced a second partial differential
equation for transition probabilities of Markov processes order
with continuous trajectories (i. e. diffusion processes) and thus
revealed the deep connection between theories of differential
equations and stochastic processes. The stochastic analysis created
by K. Ito (also independently by Gihman 1]) in the forties is
essentially an infinitesimal analysis for trajectories of
stochastic processes. By virtue of Ito's stochastic differential
equations one can construct diffusion processes via direct
probabilistic methods and treat them as function als of Brownian
paths (i. e. the Wiener functionals)."
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