34 citations · 168 across the 14 of their papers we have counts for
11 papers · 1 filter
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
Johan Dahlin, Adrian Wills, Brett Ninness
Pseudo-marginal Metropolis-Hastings (pmMH) is a versatile algorithm for sampling from target distributions which are not easy to evaluate point-wise. However, pmMH requires good pr…
Constructing Metropolis-Hastings proposals using damped BFGS updates
Johan Dahlin, Adrian Wills, Brett Ninness
The computation of Bayesian estimates of system parameters and functions of them on the basis of observed system performance data is a common problem within system identification.…
Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
Johan Dahlin, Fredrik Lindsten, Joel Kronander +1
Pseudo-marginal Metropolis-Hastings (pmMH) is a powerful method for Bayesian inference in models where the posterior distribution is analytical intractable or computationally costl…
Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models
Johan Dahlin, Thomas B. Schön
This tutorial provides a gentle introduction to the particle Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear state-space models together with a software im…
Bayesian optimisation for fast approximate inference in state-space models with intractable likelihoods
Johan Dahlin, Mattias Villani, Thomas B. Schön
We consider the problem of approximate Bayesian parameter inference in non-linear state-space models with intractable likelihoods. Sequential Monte Carlo with approximate Bayesian…
Sequential Monte Carlo Methods for System Identification
Thomas B. Schön, Fredrik Lindsten, Johan Dahlin +4
One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo…
Quasi-Newton particle Metropolis-Hastings
Johan Dahlin, Fredrik Lindsten, Thomas B. Schön
Particle Metropolis-Hastings enables Bayesian parameter inference in general nonlinear state space models (SSMs). However, in many implementations a random walk proposal is used an…
Newton-based maximum likelihood estimation in nonlinear state space models
Manon Kok, Johan Dahlin, Thomas B. Schön +1
Maximum likelihood (ML) estimation using Newton's method in nonlinear state space models (SSMs) is a challenging problem due to the analytical intractability of the log-likelihood…
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…