34 citations · 168 across the 14 of their papers we have counts for
3 papers · 2 filters
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…