Markov chain approximation method

In numerical methods for stochastic differential equations, the Markov chain approximation method (MCAM) belongs to the several numerical (schemes) approaches used in stochastic control theory. Regrettably the simple adaptation of the deterministic schemes for matching up to stochastic models such as the Runge–Kutta method does not work at all. The basic idea of the MCAM is to approximate the original by a chosen on a . In case of need, one must as well approximate the for one that matches up the Markov chain chosen to approximate the original stochastic process.