Finite difference methods for the weak solutions of the Kolmogorov equation for the density of both diffusion and conditional diffusion processes
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Publication:1227270
DOI10.1016/0022-247X(76)90109-8zbMath0329.65059OpenAlexW2028527546MaRDI QIDQ1227270
Publication date: 1976
Published in: Journal of Mathematical Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/0022-247x(76)90109-8
Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Applications to the sciences (65Z05) Parabolic equations and parabolic systems (35K99)
Related Items (8)
On the Discretization of Some Nonlinear Fokker--Planck--Kolmogorov Equations and Applications ⋮ Three ways to solve partial differential equations with neural networks — A review ⋮ A numerical approach to Kolmogorov equation in high dimension based on Gaussian analysis ⋮ Continuous-time Random Walks for the Numerical Solution of Stochastic Differential Equations ⋮ SPECTRWM: Spectral Random Walk Method for the Numerical Solution of Stochastic Partial Differential Equations ⋮ Probabilistic methods for finite difference approximations to degenerate elliptic and parabolic equations with Neumann and Dirichlet boundary conditions ⋮ Sticky Brownian Motion and Its Numerical Solution ⋮ Solving the Kolmogorov PDE by means of deep learning
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- On the weak convergence of interpolated Markov chains to a diffusion
- Approximations, existence, and numerical procedures for optimal stochastic controls
- Dynamical equations for optimal nonlinear filtering
- Probability limit theorems and the convergence of finite difference approximations of partial differential equations
- A Survey of Some Applications of Probability and Stochastic Control Theory to Finite Difference Methods for Degenerate Elliptic and Parabolic Equations
- On the Differential Equations Satisfied by Conditional Probablitity Densities of Markov Processes, with Applications
- On the optimal filtering of diffusion processes
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