Wojciech Niemiro (Nicolaus Copernicus University, Torun, and University of Warsaw, Poland): Adaptive Monte Carlo Maximum Likelihood based on Importance Sampling with Resampling

We consider the problem of computing the Maximum Likelihood estimate in a model where the likelihood involves an intractable normalizing constant. This constant is computed via Monte Carlo. Straightforward Importance Sampling in this context is usually inefficient because of degeneracy of weights. We propose an adaptive scheme, in which resampling combined with MCMC is used to overcome the weights degeneracy problem. Asymptotic behaviour of our algorithm is examined.

Joint work with Jan Palczewski

Keywords: Adaptation, Maximum Likelihood, Normalizing constant, Martingale differences

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