34 citations · 168 across the 14 of their papers we have counts for
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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…
Marginalizing Gaussian Process Hyperparameters using Sequential Monte Carlo
Andreas Svensson, Johan Dahlin, Thomas B. Schön
Gaussian process regression is a popular method for non-parametric probabilistic modeling of functions. The Gaussian process prior is characterized by so-called hyperparameters, wh…