123 citations · 876 across the 50 of their papers we have counts for
6 papers · 1 filter
Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM
Roger Frigola, Fredrik Lindsten, Thomas B. Schön +1
Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems. They comprise a Bayesian nonparametric representation of the dyna…
Recursive maximum likelihood identification of jump Markov nonlinear systems
Emre Özkan, Fredrik Lindsten, Carsten Fritsche +1
In this contribution, we present an online method for joint state and parameter estimation in jump Markov non-linear systems (JMNLS). State inference is enabled via the use of part…
Particle filter-based Gaussian process optimisation for parameter inference
Johan Dahlin, Fredrik Lindsten
We propose a novel method for maximum likelihood-based parameter inference in nonlinear and/or non-Gaussian state space models. The method is an iterative procedure with three step…
Particle Metropolis-Hastings using gradient and Hessian information
Johan Dahlin, Fredrik Lindsten, Thomas B. Schön
Particle Metropolis-Hastings (PMH) allows for Bayesian parameter inference in nonlinear state space models by combining Markov chain Monte Carlo (MCMC) and particle filtering. The…
Inference in Gaussian models with missing data using Equalisation Maximisation
Johan Dahlin, Fredrik Lindsten, Thomas B. Schön
Equalisation Maximisation (EqM) is an algorithm for estimating parameters in auto-regressive (AR) models where some fraction of the data is missing. It has previously been shown th…
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC
Roger Frigola, Fredrik Lindsten, Thomas B. Schön +1
State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inf…