Second-order discretization schemes of stochastic differential systems for the computation of the invariant law
@article{Talay1990SecondorderDS, title={Second-order discretization schemes of stochastic differential systems for the computation of the invariant law}, author={Denis Talay}, journal={Stochastics and Stochastics Reports}, year={1990}, volume={29}, pages={13-36} }
We Discretize in Time With Step-Size h a Stochastic Differential Equation Whose Solution has a Unique Invariant Probability Measure is the Solution of the Discretized System, we Give an Estimate of in Terms of h for Several Discretization Methods. In Particular, Methods Which are of Second Order for the Approximation of in Finite Time are Shown to be Generically of Second Order for the Ergodic Criterion(1).
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References
SHOWING 1-10 OF 25 REFERENCES
Discretization and simulation of stochastic differential equations
- Mathematics
- 1985
We discuss both pathwise and mean-square convergence of several approximation schemes to stochastic differential equations. We then estimate the corresponding speeds of convergence, the error being…
An asymptotically efficient difference formula for solving stochastic differential equations
- Mathematics
- 1986
Time discretisations of teh vector stochastic differential equation are considered, where (y t) is a continuous scalar process whose distribution is absolutely continuous with respect to Wiener…
Stochastic Stability of Differential Equations
- Mathematics
- 1980
Boundedness in Probability and Stability of Stochastic Processes Defined by Differential Equations.- 2.Stationary and Periodic Solutions of Differential Equations. 3.Markov Processes and Stochastic…
Numerical Treatment of Stochastic Differential Equations
- Mathematics
- 1982
We define general Runge–Kutta approximations for the solution of stochastic differential equations (sde). These approximations are proved to converge in quadratic mean to the solution of an sde with…
Weak Approximation of Solutions of Systems of Stochastic Differential Equations
- Computer Science, Mathematics
- 1986
As already mentioned in the Introduction, in cases when the modeling of solutions is intended for the application of Monte-Carlo methods we can refrain from mean-square approximations and use…
Asymptotic analysis of P.D.E.s with wide–band noise disturbances, and expansion of the moments
- Mathematics
- 1984
We present an asymptotic analysis -in the “ white-noise limit”- of a linear parabolic partial differential equation, whose coefficients are perturbed by a wide-band noise. After having studied some…
Approximation of upper Lyapunov exponents of bilinear stochastic differential systems
- Mathematics
- 1991
A bilinear Stochastic Differential System is considered whose solution starting from x is denoted by $(X(t,x))$; it is supposed that its (upper) Lyapunov exponent $\lambda $ exists. The purpose of…
The existence of moments for stationary Markov chains
- MathematicsJournal of Applied Probability
- 1983
We give conditions under which the stationary distribution π of a Markov chain admits moments of the general form ∫ f(x)π(dx), where f is a general function; specific examples include f(x) = xr and…
Stochastic differential systems : filtering and control : proceedings of the IFIP-WG 7/1 working conference, Vilnius, Lithuania, USSR, Aug. 28-Sept. 2, 1978
- Mathematics
- 1980
Some estimation problems for stochastic differential equations.- Applications of stochastic differential equations to the description of turbulent equations.- On semimartingales with values in…