Abstract
This paper is a brief introduction to the numerical approximation of weak and strong solutions of stochastic differential equations. These have application in many fields including signal processing, stochastic control, financial mathematics and theoretical physics. In particular, weak solutions can be used as the basis of Monte Carlo methods for certain partial differential equations. Weak and strong solutions are different concepts and require different approximation techniques based on different convergence criteria.
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