123 citations · 876 across the 50 of their papers we have counts for
6 papers · 1 filter
Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations
Andreas Svensson, Fredrik Lindsten, Thomas B. Schön
When classical particle filtering algorithms are used for maximum likelihood parameter estimation in nonlinear state-space models, a key challenge is that estimates of the likeliho…
Pseudo-extended Markov chain Monte Carlo
Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone +1
Sampling from posterior distributions using Markov chain Monte Carlo (MCMC) methods can require an exhaustive number of iterations, particularly when the posterior is multi-modal a…
Distributed, scalable and gossip-free consensus optimization with application to data analysis
Sina Khoshfetrat Pakazad, Christian A. Naesseth, Fredrik Lindsten +1
Distributed algorithms for solving additive or consensus optimization problems commonly rely on first-order or proximal splitting methods. These algorithms generally come with rest…
Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo
Thomas B. Schön, Andreas Svensson, Lawrence Murray +1
Probabilistic modeling provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. We are concerned with the problem of learning pro…
Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution
Andreas Svensson, Thomas B. Schön, Fredrik Lindsten
Probabilistic (or Bayesian) modeling and learning offers interesting possibilities for systematic representation of uncertainty using probability theory. However, probabilistic lea…
Smoothing with Couplings of Conditional Particle Filters
Pierre E. Jacob, Fredrik Lindsten, Thomas B. Schön
In state space models, smoothing refers to the task of estimating a latent stochastic process given noisy measurements related to the process. We propose an unbiased estimator of s…