Numerical methods for stochastic differential equations based on Gaussian mixture
| dc.contributor.author | Li, L | |
| dc.contributor.author | Lu, J | |
| dc.contributor.author | Mattingly, JC | |
| dc.contributor.author | Wang, L | |
| dc.date.accessioned | 2019-01-23T14:18:12Z | |
| dc.date.available | 2019-01-23T14:18:12Z | |
| dc.date.updated | 2019-01-23T14:18:11Z | |
| dc.description.abstract | We develop in this work a numerical method for stochastic differential equations (SDEs) with weak second order accuracy based on Gaussian mixture. Unlike the conventional higher order schemes for SDEs based on It^o-Taylor expansion and iterated It^o integrals, the proposed scheme approximates the probability measure $\mu(X^{n+1}|X^n=x_n)$ by a mixture of Gaussians. The solution at next time step $X^{n+1}$ is then drawn from the Gaussian mixture with complexity linear in the dimension $d$. This provides a new general strategy to construct efficient high weak order numerical schemes for SDEs. | |
| dc.identifier.uri | ||
| dc.publisher | International Press of Boston | |
| dc.subject | math.NA | |
| dc.subject | math.NA | |
| dc.subject | math.PR | |
| dc.subject | 65, 60 | |
| dc.title | Numerical methods for stochastic differential equations based on Gaussian mixture | |
| dc.type | Journal article | |
| duke.contributor.orcid | Lu, J|0000-0001-6255-5165 | |
| duke.contributor.orcid | Mattingly, JC|0000-0002-1819-729X | |
| duke.contributor.orcid | Wang, L|0000-0002-9130-0505 | |
| pubs.organisational-group | Trinity College of Arts & Sciences | |
| pubs.organisational-group | Duke | |
| pubs.organisational-group | Chemistry | |
| pubs.organisational-group | Mathematics | |
| pubs.organisational-group | Physics | |
| pubs.organisational-group | Student |