Markov chain approximation method

In numerical methods for , the Markov chain approximation method (MCAM) belongs to the several numerical (schemes) approaches used in . 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.

It is a powerful and widely usable set of ideas, due to the current infancy of stochastic control it might be even said 'insights.' for numerical and other approximations problems in . They represent counterparts from deterministic control theory such as optimal control theory.

The basic idea of the MCAM is to approximate the original controlled process by a chosen controlled markov process on a finite state space. In case of need, one must as well approximate the cost function for one that matches up the Markov chain chosen to approximate the original stochastic process.

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