alu042/sde-higham

Some basic algorithms for stochastic differential equations in Python/NumPy

Some basic algorithms for stochastic differential equations in NumPy

Overview

Modified from the MATLAB versions in Higham “An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations”, SIAM Review, Vol. 43, No. 3, 2001. http://www.caam.rice.edu/~cox/stoch/dhigham.pdf

Details about the algorithms can be found in the paper.

List of algorithms

Filename	Description
bpath2.py	Simulation of a Brownian path
bpath3.py	Function along a Brownain path
stint.py	Approximate stochastic integrals
em.py	Euler-Maruyama method on linear SDE
emstrong.py	Test strong convergence of Euler-Maruyama
emweak.py	Test weak convergence of Euler-Maruyama
milstrong.py	Test strong convergence of Milstein method
stab.py	Mean-square and asymptotic stability test for E-M
chain.py	Test stochastic chain rule
milstein-3d.py	Milstein’s method applied to a 3D SDE (not part of SIAM paper)