882 citations · 2.6k across the 89 of their papers we have counts for
14 papers · 1 filter
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…
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models
John-Alexander M. Assael, Niklas Wahlström, Thomas B. Schön +1
Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems…
Nonlinear State Space Model Identification Using a Regularized Basis Function Expansion
Andreas Svensson, Thomas B. Schön, Arno Solin +1
This paper is concerned with black-box identification of nonlinear state space models. By using a basis function expansion within the state space model, we obtain a flexible struct…
Modeling and interpolation of the ambient magnetic field by Gaussian processes
Arno Solin, Manon Kok, Niklas Wahlström +2
Anomalies in the ambient magnetic field can be used as features in indoor positioning and navigation. By using Maxwell's equations, we derive and present a Bayesian non-parametric…
Computationally Efficient Bayesian Learning of Gaussian Process State Space Models
Andreas Svensson, Arno Solin, Simo Särkkä +1
Gaussian processes allow for flexible specification of prior assumptions of unknown dynamics in state space models. We present a procedure for efficient Bayesian learning in Gaussi…
Rao-Blackwellized particle smoothers for conditionally linear Gaussian models
Fredrik Lindsten, Pete Bunch, Simo Särkkä +2
Sequential Monte Carlo (SMC) methods, such as the particle filter, are by now one of the standard computational techniques for addressing the filtering problem in general state-spa…
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…
Nonlinear state space smoothing using the conditional particle filter
Andreas Svensson, Thomas B. Schön, Manon Kok
To estimate the smoothing distribution in a nonlinear state space model, we apply the conditional particle filter with ancestor sampling. This gives an iterative algorithm in a Mar…
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…
Nested Sequential Monte Carlo Methods
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC gen…
From Pixels to Torques: Policy Learning with Deep Dynamical Models
Niklas Wahlström, Thomas B. Schön, Marc Peter Deisenroth
Data-efficient learning in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. In this paper, we…
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…