Stochastic Evolution Systems by Boris L Rozovsky
This monograph, now in a thoroughly revised second edition, develops the theory of stochastic calculus in Hilbert spaces and applies the results to the study of generalized solutions of stochastic parabolic equations. The emphasis lies on second-order stochastic parabolic equations and their connection to random dynamical systems.-
Foundations of Modern Probability
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Geometry of Level Sets of Random Fields, Kac–Rice Formulas, Hermite Expansions and Applications
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Dynamics on Graphs
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Hybrid Switching Diffusions
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Nonlinear Expectations and Stochastic Calculus under Uncertainty
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Probability on Compact Lie Groups
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Measure-Valued Branching Markov Processes
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Pseudo-Regularly Varying Functions and Generalized Renewal Processes
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Stochastic Disorder Problems
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Dirichlet Forms Methods for Poisson Point Measures and Lévy Processes
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Analysis and Approximation of Rare Events
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Random Ordinary Differential Equations and Their Numerical Solution
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Ambit Stochastics
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Mathematical Control Theory for Stochastic Partial Differential Equations
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Dirichlet Forms Methods for Poisson Point Measures and Levy Processes
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Lectures on Monte Carlo Theory
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Stochastic Control Theory
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Random Measures, Theory and Applications
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Yosida Approximations of Stochastic Differential Equations in Infinite Dimensions and Applications
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Random Walks in the Quarter Plane
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Stochastic Flows and Jump-Diffusions
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Stochastic Multi-Stage Optimization
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The Quasispecies Equation and Classical Population Models
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Asymptotic Theory of Weakly Dependent Random Processes
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Stable Convergence and Stable Limit Theorems
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Stochastic Integration in Banach Spaces
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Continuous-Time Markov Decision Processes
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Dynamic Markov Bridges and Market Microstructure
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Limit Theorems for Multi-Indexed Sums of Random Variables
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Markov Renewal and Piecewise Deterministic Processes
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Convex Stochastic Optimization
“A remarkable quality of this monograph is that the results are stated and proved with a great level of generality and rigor. The reader will find many interesting results, as well as lots of long and technical proofs … .” (Charles-Edouard Bréhier, Mathematical Reviews, October, 2019)
Boris Rozovsky earned a Master’s degree in Probability and Statistics, followed by a PhD in Physical and Mathematical Sciences, both from the Moscow State (Lomonosov) University. He was Professor of Mathematics and Director of the Center for Applied Mathematical Sciences at the University of Southern California. Currently, he is the Ford Foundation Professor of Applied Mathematics at Brown University.
Sergey Lototsky earned a Master’s degree in Physics in 1992 from the Moscow Institute of Physics and Technology, followed by a PhD in Applied Mathematics in 1996 from the University of Southern California. After a year-long post-doc at the Institute for Mathematics and its Applications and a three-year term as a Moore Instructor at MIT, he returned to the department of Mathematics at USC as a faculty member in 2000. He specializes in stochastic analysis, with emphasis on stochastic differential equation. He supervised more than 10 PhD students and had visiting positions at the Mittag-Leffler Institute in Sweden and at several universities in Israel and Italy.
| SKU | Unavailable |
| ISBN 13 | 9783319948928 |
| ISBN 10 | 331994892X |
| Title | Stochastic Evolution Systems |
| Author | Boris L Rozovsky |
| Series | Probability Theory And Stochastic Modelling |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer International Publishing AG |
| Year published | 2018-10-15 |
| Number of pages | 330 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |