On solving stochastic differential equations
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Abstract
This paper proposes a new approach to solving Ito stochastic differential equations. It is based on the well-known Monte Carlo methods for solving integral equations (Neumann–Ulam scheme, Markov chain Monte Carlo). The estimates of the solution for a wide class of equations do not have a bias, which distinguishes them from estimates based on difference approximations (Euler, Milstein methods, etc.).
Funding source: Russian Science Foundation
Award Identifier / Grant number: 17-01-00267
Funding statement: Supported by the Russian Science Foundation under the research grant 17-01-00267.
References
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© 2019 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- A third-order weak approximation of multidimensional Itô stochastic differential equations
- Particle diffusion Monte Carlo (PDMC)
- Random walk on rectangles and parallelepipeds algorithm for solving transient anisotropic drift-diffusion-reaction problems
- Analysis of a non-Markovian queueing model: Bayesian statistics and MCMC methods
- On solving stochastic differential equations
- On the sample-mean method for computing hyper-volumes
- Comparing M/G/1 queue estimators in Monte Carlo simulation through the tested generator “getRDS” and the proposed “getLHS” using variance reduction
Articles in the same Issue
- Frontmatter
- A third-order weak approximation of multidimensional Itô stochastic differential equations
- Particle diffusion Monte Carlo (PDMC)
- Random walk on rectangles and parallelepipeds algorithm for solving transient anisotropic drift-diffusion-reaction problems
- Analysis of a non-Markovian queueing model: Bayesian statistics and MCMC methods
- On solving stochastic differential equations
- On the sample-mean method for computing hyper-volumes
- Comparing M/G/1 queue estimators in Monte Carlo simulation through the tested generator “getRDS” and the proposed “getLHS” using variance reduction
