10.6084/m9.figshare.7874177.v1 Pierre E. Jacob Pierre E. Jacob Fredrik Lindsten Fredrik Lindsten Thomas B. Schön Thomas B. Schön Smoothing with Couplings of Conditional Particle Filters Taylor & Francis Group 2019 couplings particle filtering particle smoothing debiasing techniques parallel computation 2019-03-21 14:16:28 Dataset https://tandf.figshare.com/articles/dataset/Smoothing_with_Couplings_of_Conditional_Particle_Filters/7874177 <p>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 confidence intervals 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 chains, with a Markov chain Monte Carlo algorithm for smoothing. The resulting procedure is widely applicable and we show in numerical experiments that the removal of the bias comes at a manageable increase in variance. We establish the validity of the proposed estimators under mild assumptions. Numerical experiments are provided on toy models, including a setting of highly-informative observations, and for a realistic Lotka-Volterra model with an intractable transition density.</p>