Kernel embedded nonlinear observational mappings in the variational mapping particle filter

no code implementations • 29 Jan 2019 • Manuel Pulido, Peter Jan vanLeeuwen, Derek J. Posselt

In this work, we evaluate nonlinear observational mappings in the variational mapping method using two approximations that avoid the adjoint, an ensemble based approximation in which the gradient is approximated by the particle covariances in the state and observational spaces the so-called ensemble space and an RKHS approximation in which the observational mapping is embedded in an RKHS and the gradient is derived there.

Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filter

1 code implementation • 29 May 2018 • Manuel Pulido, Peter Jan vanLeeuwen

In this work, a novel sequential Monte Carlo filter is introduced which aims at efficient sampling of high-dimensional state spaces with a limited number of particles.

Bayesian Inference Sequential Bayesian Inference +1