# Smoothing With Couplings of Conditional Particle Filters

@article{Jacob2017SmoothingWC, title={Smoothing With Couplings of Conditional Particle Filters}, author={P. Jacob and F. Lindsten and Thomas Bo Sch{\"o}n}, journal={Journal of the American Statistical Association}, year={2017}, volume={115}, pages={721 - 729} }

Abstract In state–space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of smoothing expectations. The lack-of-bias property has methodological benefits: independent estimators can be generated in parallel, and CI can be constructed from the central limit theorem to quantify the approximation error. To design unbiased estimators, we combine a generic debiasing technique for Markov… Expand

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