34 citations · 146 across the 10 of their papers we have counts for
12 papers
Learning dynamical systems with particle stochastic approximation EM
Andreas Lindholm, Fredrik Lindsten
We present the particle stochastic approximation EM (PSAEM) algorithm for learning of dynamical systems. The method builds on the EM algorithm, an iterative procedure for maximum l…
How consistent is my model with the data? Information-Theoretic Model Check
Andreas Svensson, Dave Zachariah, Thomas B. Schön
The choice of model class is fundamental in statistical learning and system identification, no matter whether the class is derived from physical principles or is a generic black-bo…
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…
A flexible state space model for learning nonlinear dynamical systems
Andreas Svensson, Thomas B. Schön
We consider a nonlinear state-space model with the state transition and observation functions expressed as basis function expansions. The coefficients in the basis function expansi…