Riassunto
The central and distinguishing feature shared by all the contributions made by K. Ito is the extraordinary insight which they convey. Reading his papers, one should try to picture the intellectual setting in which he was working. At the time when he was a student in Tokyo during the late 1930s, probability theory had only recently entered the age of continuous-time stochastic processes: N. Wiener had accomplished his amazing construction little more than a decade earlier (Wiener, N. , "Differential space," J. Math. Phys. 2, (1923)), Levy had hardly begun the mysterious web he was to eventually weave out of Wiener's P~!hs, the generalizations started by Kolmogorov (Kol mogorov, A. N. , "Uber die analytische Methoden in der Wahrscheinlichkeitsrechnung," Math Ann. 104 (1931)) and continued by Feller (Feller, W. , "Zur Theorie der stochastischen Prozesse," Math Ann. 113, (1936)) appeared to have little if anything to do with probability theory, and the technical measure-theoretic tours de force of J. L. Doob (Doob, J. L. , "Stochastic processes depending on a continuous parameter, " TAMS 42 (1937)) still appeared impregnable to all but the most erudite. Thus, even at the established mathematical centers in Russia, Western Europe, and America, the theory of stochastic processes was still in its infancy and the student who was asked to learn the subject had better be one who was ready to test his mettle.
Contenuti
[1] On Stochastic Processes (Infinitely divisible laws of probability).- [2] Differential Equations Determining a Markoff Process.- [3] On the Ergodicity of a Certain Stationary Process.- [4] A Kinematic Theory of Turbulence.- [5] On the Normal Stationary Process with no Hysteresis.- [7] Stochastic Integral.- [9] On a Stochastic Integral Equation.- [10] Stochastic Differential Equations in a Differentiable Manifold.- [11] Brownian Motions in a Lie Group.- [12] On Stochastic Differential Equations.- [13] On a Formula Concerning Stochastic Differentials.- [14] Multiple Wiener Integral.- [15] Stochastic Differential Equations in a Differentiable Manifold.- [16] Stationary Random Distributions.- [17] Complex Multiple Wiener Integral.- [18] Isotropic Random Current.- [19] Spectral Type of the Shift Transformation of Differential Processes with Stationary Increments.- [20] Potentials and the Random Walk.- [21] Wiener Integral and Feynman Integral.- [22] Construction of Diffusions.- [23] The Brownian Motion and Tensor Fields on Riemannian Manifold.- [24] Brownian Motions on a Half Line.- [25] The Expected Number of Zeros of Continuous Stationary Gaussian Processes.- [26] On Stationary Solutions of a Stochastic Differential Equation.- [27] Transformation of Markov Processes by Multiplicative Functionals.- [28] The Canonical Modification of Stochastic Processes.- [29] On the Convergence of Sums of Independent Banach Space Valued Random Variables.- [30] Generalized Uniform Complex Measures in the Hilbertian Metric Space with their Application to the Feynman Integral.- [31] On the Oscillation Functions of Gaussian Processes.- [32] Canonical Measurable Random Functions.- [33] The Topological Support of a Gauss Measure on Hilbert Space.- [34] Poisson Point Processes Attached to Markov Processes.- [37] Stochastic Differentials.- [38] Stochastic Parallel Displacement.- [40] Extension of Stochastic Integrals.- [44] Infinite Dimensional Ornstein-Uhlenbeck Processes.- [45] Regularization of Linear Random Functionals (with M. Nawata).- [46] Distribution-Valued Processes Arising from Independent Brownian Motions.
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