123 citations · 876 across the 50 of their papers we have counts for
4 papers · 1 filter
High-dimensional Filtering using Nested Sequential Monte Carlo
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering. However, SMC without good proposal distributions struggle in…
Coupling of Particle Filters
Pierre E. Jacob, Fredrik Lindsten, Thomas B. Schön
Particle filters provide Monte Carlo approximations of intractable quantities such as point-wise evaluations of the likelihood in state space models. In many scenarios, the interes…
Pseudo-Marginal Hamiltonian Monte Carlo
Johan Alenlöv, Arnaud Doucet, Fredrik Lindsten
Bayesian inference in the presence of an intractable likelihood function is computationally challenging. When following a Markov chain Monte Carlo (MCMC) approach to approximate th…
Interacting Particle Markov Chain Monte Carlo
Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten +4
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method based on an interacting pool of standard and conditional sequential Monte Carlo samplers. Like r…