123 citations · 876 across the 50 of their papers we have counts for
6 papers · 1 filter
Identification of jump Markov linear models using particle filters
Andreas Svensson, Thomas B. Schön, Fredrik Lindsten
Jump Markov linear models consists of a finite number of linear state space models and a discrete variable encoding the jumps (or switches) between the different linear models. Ide…
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…
Capacity estimation of two-dimensional channels using Sequential Monte Carlo
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
We derive a new Sequential-Monte-Carlo-based algorithm to estimate the capacity of two-dimensional channel models. The focus is on computing the noiseless capacity of the 2-D one-i…
Uniform ergodicity of the Particle Gibbs sampler
Fredrik Lindsten, Randal Douc, Eric Moulines
The particle Gibbs (PG) sampler is a systematic way of using a particle filter within Markov chain Monte Carlo (MCMC). This results in an off-the-shelf Markov kernel on the space o…
Sequential Monte Carlo for Graphical Models
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM w…
Particle Gibbs with Ancestor Sampling
Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön
Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov ch…