Applied Stochastic Analysis: Proceedings of a US-French Workshop, Rutgers University, New Brunswick, N.J., April 29-May 2, 1991Ioannis Karatzas, Daniel Ocone This volume contains papers presented during a four-day Workshop that took place at Rutgers University from 29 April to 2 May, 1991. The purpose of this workshop was to promote interaction among specialists in these areas byproviding for all an up-to-date picture of current issues and outstanding problems. The topics covered include singular stochasticcontrol, queuing networks, the mathematical theory of stochastic optimization and filtering, adaptive control and the estimation for random fields and its connections with simulated annealing, statistical mechanics, and combinatorial optimization. |
Contents
F BACCELLI and P CONSTANTOPOULOS | 1 |
On Bellman Equations of Ergodic Control in R | 21 |
MultiDimensional FiniteFuel Singular Stochastic Control | 38 |
Copyright | |
8 other sections not shown
Other editions - View all
Applied Stochastic Analysis: Proceedings of a US-French Workshop, Rutgers ... Ioannis Karatzas,Daniel Ocone No preview available - 1992 |
Applied Stochastic Analysis: Proceedings of a US-French Workshop, Rutgers ... Ioannis Karatzas,Daniel Ocone No preview available - 2014 |
Common terms and phrases
algorithm apply approximation assume assumptions asymptotic bounded Brownian motion compact compute consider constant continuous control problem convergence convex convex function cost function deduce define definition denote deterministic differential equations diffusion discrete dynamic programming E₁ ergodic estimate event graph exists finite formula given H₁ holds implies inequality initial condition INRIA Karatzas Kushner large deviations Lemma Lévy process linear Lyapunov exponent Markov chain martingale problem martingale representation martingale representation theorem Math Mathematics matrix nonlinear filtering numerical observation obtain optimal control Poisson process probability measure Proof of Theorem Proposition prove quadratic Rachev random measure random variables satisfies simulated annealing solution space stationary stochastic control stochastic integral Suppose Theorem 3.1 transition unique value function W₁ Wiener process X₁ zero