47 citations · 219 across the 37 of their papers we have counts for
6 papers · 1 filter
Reverse Diffusion Sequential Monte Carlo Samplers
Luhuan Wu, Yi Han, Christian A. Naesseth +1
We propose a novel sequential Monte Carlo (SMC) method for sampling from unnormalized target distributions based on a reverse denoising diffusion process. While recent diffusion-ba…
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…
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…
Sequential Monte Carlo Methods for System Identification
Thomas B. Schön, Fredrik Lindsten, Johan Dahlin +4
One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo…
Nested Sequential Monte Carlo Methods
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC gen…
Divide-and-Conquer with Sequential Monte Carlo
Fredrik Lindsten, Adam M. Johansen, Christian A. Naesseth +4
We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models. This class of algorithms adopts a divide-and-conqu…