We consider a parameter estimation problem for a Hidden Markov Model in the framework of particle filters. Using constructs from reinforcement learning for variance reduction in particle filters, a simulationbased sc...
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ISBN:
(纸本)9781479935901
We consider a parameter estimation problem for a Hidden Markov Model in the framework of particle filters. Using constructs from reinforcement learning for variance reduction in particle filters, a simulation based scheme is developed for estimating the partially observed log-likelihood function. A Kiefer-Wolfowitz like stochastic approximation scheme maximizes this function over the unknown parameter. The two procedures are performed on two different time scales, emulating the alternating 'expectation' and 'maximization' operations of the EM algorithm. Numerical experiments are presented in support of the proposed scheme.
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