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Computing the filter derivative using Sequential Monte Carlo

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If you have a question about this talk, please contact Edward Mottram.

Sequential Monte Carlo (SMC) methods are widely used computational tools for Bayesian inference in nonlinear non-Gaussian state space models. In this talk, I present a SMC algorithm for computing the derivative of the optimal filter in a Hidden Markov Model and study its stability. I will discuss applications to parameter estimation and optimal control.

This talk is part of the Cambridge Analysts' Knowledge Exchange series.

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