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Forward Smoothing using Sequential Monte Carlo with Application to Recursive Parameter Estimation

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Sequential Monte Carlo (SMC) methods are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. We propose a new SMC algorithm to compute the expectation of additive functionals recursively. Compared to the standard path space SMC estimator whose asymptotic variance increases quadratically with time even under favourable mixing assumptions, the asymptotic variance of the proposed SMC estimator only increases linearly with time. We show how this allows us to perform recursive parameter estimation using SMC algorithms which do not not suffer from the particle path degeneracy problem.

Joint work with P. Del Moral (INRIA Bordeaux) & S.S. Singh (Cambridge University)

This talk is part of the Statistics series.

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