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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