Numerical Methods for Stochastic ProcessesJohn Wiley & Sons, 14 січ. 1994 р. - 384 стор. Gives greater rigor to numerical treatments of stochastic models. Contains Monte Carlo and quasi-Monte Carlo techniques, simulation of major stochastic procedures, deterministic methods adapted to Markovian problems and special problems related to stochastic integral and differential equations. Simulation methods are given throughout the text as well as numerous exercises. |
Зміст
Preliminaries | 1 |
C | 14 |
WienerLévy Calculus | 26 |
Computation of Expectations in Finite Dimension | 34 |
Numerical Computation of Conditional Expectation | 95 |
B Representations of Stationary Fields | 142 |
E Point Processes | 221 |
B 4 | 230 |
Stochastic Differential Equations and Brownian Functionals | 269 |
F Application of the Shift Method to Multiple Wiener Integrals and to Solutions of SDEs | 319 |
Загальні терміни та фрази
algorithm applications approximation B₁ Borel sequence Bouleau bounded measure Brownian motion Carlo method Chapter compact compute continuous function converges d-dimensional defined Definition Dellacherie denote density discretization distribution ergodic exists Feller finite follows formula func function f Gaussian independent iterated Ito's Lebesgue Lemma Let f Lévy linear Lipschitz low-discrepancy sequences Markov processes martingale mathematics Meyer Monte Carlo Monte Carlo method Notes in Math o-field open set Polish space polynomials Prob Probabilités probability measure probability space Proof Proposition PSII quasi-Monte Carlo methods random variable recursive réduite result Riemann integrable satisfies Section semi-group shift method simulation solution spectral Springer stationary process Statistics stochastic differential equations stochastic integrals stochastique sub-Markov kernel subset Suppose Talay theorem theory tion topology unique V₁ vector Wiener space X₁ Y₁ zero
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