Search Results - "Lindsten, F."

Blocking strategies and stability of particle Gibbs samplers by SINGH, S. S., LINDSTEN, F., MOULINES, E.

“…Sampling from the posterior probability distribution of the latent states of a hidden Markov model is nontrivial even in the context of Markov chain Monte…”
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Divide-and-Conquer With Sequential Monte Carlo by Lindsten, F., Johansen, A. M., Naesseth, C. A., Kirkpatrick, B., Schön, T. B., Aston, J. A. D., Bouchard-Côté, A.

“…We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models. This class of algorithms…”
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On the use of backward simulation in the particle Gibbs sampler by Lindsten, F., Schön, T. B.

“…The particle Gibbs (PG) sampler was introduced in [1] as a way to incorporate a particle filter (PF) in a Markov chain Monte Carlo (MCMC) sampler. The…”
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Adaptive stopping for fast particle smoothing by Taghavi, Ehsan, Lindsten, Fredrik, Svensson, Lennart, Schon, Thomas B.

“…Particle smoothing is useful for offline state inference and parameter learning in nonlinear/non-Gaussian state-space models. However, many particle smoothers,…”
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A General Framework for Ensemble Distribution Distillation by Lindqvist, Jakob, Olmin, Amanda, Lindsten, Fredrik, Svensson, Lennart

“…Ensembles of neural networks have shown to give better predictive performance and more reliable uncertainty estimates than individual networks. Additionally,…”
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Generalised Active Learning With Annotation Quality Selection by Lindqvist, Jakob, Olmin, Amanda, Svensson, Lennart, Lindsten, Fredrik

“…In this paper we promote a general formulation of active learning (AL), wherein the typically binary decision to annotate a point or not is extended to…”
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Clustering using sum-of-norms regularization: With application to particle filter output computation by Lindsten, F., Ohlsson, H., Ljung, L.

“…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)…”
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Particle Filtering for Network-Based Positioning Terrestrial Radio Networks by Gunnarsson, F, Lindsten, F, Carlsson, N

“…There is strong interest in positioning in wireless networks, partly to support end user service needs, but also to support network management with…”
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Identification of mixed linear/nonlinear state-space models by Lindsten, F, Schön, Thomas B

“…The primary contribution of this paper is an algorithm capable of identifying parameters in certain mixed linear/nonlinear state-space models, containing…”
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