123 citations · 876 across the 50 of their papers we have counts for
25 papers · 1 filter
Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method
Anna Wigren, Johan Wågberg, Fredrik Lindsten +2
Identification of nonlinear systems is a challenging problem. Physical knowledge of the system can be used in the identification process to significantly improve the predictive per…
Parameter elimination in particle Gibbs sampling
Anna Wigren, Riccardo Sven Risuleo, Lawrence Murray +1
Bayesian inference in state-space models is challenging due to high-dimensional state trajectories. A viable approach is particle Markov chain Monte Carlo, combining MCMC and seque…
Particle filter with rejection control and unbiased estimator of the marginal likelihood
Jan Kudlicka, Lawrence M. Murray, Thomas B. Schön +1
We consider the combined use of resampling and partial rejection control in sequential Monte Carlo methods, also known as particle filters. While the variance reducing properties o…
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…
Improving the particle filter in high dimensions using conjugate artificial process noise
Anna Wigren, Lawrence Murray, Fredrik Lindsten
The particle filter is one of the most successful methods for state inference and identification of general non-linear and non-Gaussian models. However, standard particle filters s…
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…