882 citations · 2.6k across the 89 of their papers we have counts for
26 papers · 1 filter
Nonlinear System Identification: Learning while respecting physical models using a sequential Monte Carlo method
Anna Wigren, Johan Wågberg, Fredrik Lindsten +2
Identification of nonlinear systems is a challenging problem. Physical knowledge of the system can be used in the identification process to significantly improve the predictive per…
Particle filter with rejection control and unbiased estimator of the marginal likelihood
Jan Kudlicka, Lawrence M. Murray, Thomas B. Schön +1
We consider the combined use of resampling and partial rejection control in sequential Monte Carlo methods, also known as particle filters. While the variance reducing properties o…
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling
Jan Kudlicka, Lawrence M. Murray, Fredrik Ronquist +1
We consider probabilistic programming for birth-death models of evolution and introduce a new widely-applicable inference method that combines an extension of the alive particle fi…
Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations
Andreas Svensson, Fredrik Lindsten, Thomas B. Schön
When classical particle filtering algorithms are used for maximum likelihood parameter estimation in nonlinear state-space models, a key challenge is that estimates of the likeliho…
Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo
Thomas B. Schön, Andreas Svensson, Lawrence Murray +1
Probabilistic modeling provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. We are concerned with the problem of learning pro…
Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution
Andreas Svensson, Thomas B. Schön, Fredrik Lindsten
Probabilistic (or Bayesian) modeling and learning offers interesting possibilities for systematic representation of uncertainty using probability theory. However, probabilistic lea…
High-dimensional Filtering using Nested Sequential Monte Carlo
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering. However, SMC without good proposal distributions struggle in…
Linear System Identification via EM with Latent Disturbances and Lagrangian Relaxation
Jack Umenberger, Johan Wågberg, Ian R. Manchester +1
In the application of the Expectation Maximization algorithm to identification of dynamical systems, internal states are typically chosen as latent variables, for simplicity. In th…
A flexible state space model for learning nonlinear dynamical systems
Andreas Svensson, Thomas B. Schön
We consider a nonlinear state-space model with the state transition and observation functions expressed as basis function expansions. The coefficients in the basis function expansi…
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…
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…
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…
Identification of jump Markov linear models using particle filters
Andreas Svensson, Thomas B. Schön, Fredrik Lindsten
Jump Markov linear models consists of a finite number of linear state space models and a discrete variable encoding the jumps (or switches) between the different linear models. Ide…
Divide-and-Conquer with Sequential Monte Carlo
Fredrik Lindsten, Adam M. Johansen, Christian A. Naesseth +4
We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models. This class of algorithms adopts a divide-and-conqu…
Particle Gibbs with Ancestor Sampling
Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön
Particle Markov chain Monte Carlo (PMCMC) is a systematic way of combining the two main tools used for Monte Carlo statistical inference: sequential Monte Carlo (SMC) and Markov ch…
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…