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We propose a simple, general and computationally efficient algorithm for maximum likelihood estima- tion (MLE) of parameters in diffusion and jump-diffusion processes. This is conducted within a Monte Carlo EM-algorithm, where the smoothing distribution is computed using resampling. The results are encouraging as we can approximate the MLE well for the models studied when using simulated data. We
