Efficient discretisation of stochastic differential equations
نویسندگان
چکیده
منابع مشابه
A step towards holistic discretisation of stochastic partial differential equations
The long term aim is to use modern dynamical systems theory to derive discretisations of noisy, dissipative partial differential equations. As a first step we here consider a small domain and apply stochastic centre manifold techniques to derive a model. The approach automatically parametrises subgrid scale processes induced by spatially distributed stochastic noise. It is important to discreti...
متن کاملStochastic differential equations and integrating factor
The aim of this paper is the analytical solutions the family of rst-order nonlinear stochastic differentialequations. We dene an integrating factor for the large class of special nonlinear stochasticdierential equations. With multiply both sides with the integrating factor, we introduce a deterministicdierential equation. The results showed the accuracy of the present work.
متن کاملApplication of DJ method to Ito stochastic differential equations
This paper develops iterative method described by [V. Daftardar-Gejji, H. Jafari, An iterative method for solving nonlinear functional equations, J. Math. Anal. Appl. 316 (2006) 753-763] to solve Ito stochastic differential equations. The convergence of the method for Ito stochastic differential equations is assessed. To verify efficiency of method, some examples are ex...
متن کاملPoint-wise Integrated-RBF-based Discretisation of Differential Equations
This paper discusses a discretisation scheme which is based on point collocation and integrated radial basis function networks (IRBFNs) for the solution of elliptic differential equations (DEs). The use of IRBFNs to represent the field variable in a given DE gives several advantages over the case of using conventional RBFNs and polynomials. Some numerical examples are included for demonstration...
متن کاملEfficient importance sampling maximum likelihood estimation of stochastic differential equations
This paper considers ML estimation of a diffusion process observed discretely. Since the exact loglikelihood is generally not available, it must be approximated. We review the most efficient approaches in the literature, and point to some drawbacks. We propose to approximate the loglikelihood using the EIS strategy (Richard and Zhang, 1998), and detail its implementation for univariate homogene...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Stochastics
سال: 2019
ISSN: 1744-2508,1744-2516
DOI: 10.1080/17442508.2019.1666131