35 citations · 56 across the 4 of their papers we have counts for
4 papers
Rao-Blackwellized particle smoothers for conditionally linear Gaussian models
Fredrik Lindsten, Pete Bunch, Simo Särkkä +2
Sequential Monte Carlo (SMC) methods, such as the particle filter, are by now one of the standard computational techniques for addressing the filtering problem in general state-spa…
Particle ancestor sampling for near-degenerate or intractable state transition models
Fredrik Lindsten, Pete Bunch, Sumeetpal S. Singh +1
We consider Bayesian inference in sequential latent variable models in general, and in nonlinear state space models in particular (i.e., state smoothing). We work with sequential M…
Approximations of the Optimal Importance Density using Gaussian Particle Flow Importance Sampling
Pete Bunch, Simon Godsill
Recently developed particle flow algorithms provide an alternative to importance sampling for drawing particles from a posterior distribution, and a number of particle filters base…
The Progressive Proposal Particle Filter: Better Approximations to the Optimal Importance Density
Pete Bunch, Simon Godsill
The crucial step in designing a particle filter for a particular application is the choice of importance density. The optimal scheme is to use the conditional posterior density of…