882 citations · 2.6k across the 89 of their papers we have counts for
3 papers · 2 filters
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…
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…