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- Fredrik Lindsten, Michael I. Jordan, Thomas B. SchÃ¶n
- Journal of Machine Learning Research
- 2014

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 chain Monte Carloâ€¦ (More)

- Fredrik Lindsten
- 2013 IEEE International Conference on Acousticsâ€¦
- 2013

I present a novel method for maximum likelihood parameter estimation in nonlinear/non-Gaussian state-space models. It is an expectation maximization (EM) like method, which uses sequential Monteâ€¦ (More)

State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference and learningâ€¦ (More)

- Fredrik Lindsten, Pete Bunch, Simon J. Godsill, Thomas B. SchÃ¶n
- 2013 IEEE International Conference on Acousticsâ€¦
- 2013

We consider the smoothing problem for a class of conditionally linear Gaussian state-space (CLGSS) models, referred to as mixed linear/nonlinear models. In contrast to the better studied hierarchicalâ€¦ (More)

Clustering using sum-of-norms regularization: With application to particle filter output computation

- Fredrik Lindsten, Henrik Ohlsson, Lennart Ljung
- 2011 IEEE Statistical Signal Processing Workshopâ€¦
- 2011

We present a novel clustering method, formulated as a convex optimization problem. The method is based on over-parameterization and uses a sum-of-norms (SON) regularization to control the tradeoffâ€¦ (More)

We present a novel method in the family of particle MCMC methods that we refer to as particle Gibbs with ancestor sampling (PG-AS). Similarly to the existing PG with backward simulation (PG-BS)â€¦ (More)

This paper considers a Bayesian approach to linear system identification. One motivation is the advantage of the minimum mean square error of the associated conditional mean estimate. A furtherâ€¦ (More)

- Fredrik Lindsten
- 2011

We consider the two related problems of state inference in nonlinear dynamical systems and nonlinear system identification. More precisely, based on noisy observations from some (in general)â€¦ (More)

- Fredrik Lindsten, Pete Bunch, Simo SÃ¤rkkÃ¤, Thomas B. SchÃ¶n, Simon J. Godsill
- IEEE Journal of Selected Topics in Signalâ€¦
- 2016

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-space models. However,â€¦ (More)

- Thomas B. SchÃ¶n, Fredrik Lindsten, +4 authors Liang Dai
- 2015

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 (SMC) methods, suchâ€¦ (More)