Skip to content
Coupled Conditional Particle Filters
R C++
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.


... which stands for Coupled Conditional Particle Filters

This package accompanies the arXiv report "Smoothing with Couplings of Conditional Particle Filters" by Pierre E. Jacob, Fredrik Lindsten, Thomas B. Schön

Functions are provided to construct Rhee--Glynn estimators of smoothing functionals. For comparison, particle filters with fixed-lag and Kalman smoothers are also implemented. Examples include toy auto-regressive models, the classic nonlinear state space model from Gordon, Salmond & Smith 1993, and a prey-predator model with an intractable transition density.

Abstract of the arXiv report:

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 a realistic Lotka-Volterra model with an intractable transition density.

You can’t perform that action at this time.