Search Results - Dahlin, Johan

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  1. 1
    Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models

    Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models by Dahlin, Johan, Schön, Thomas B.

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  2. 2
    Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models

    Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models by Dahlin, Johan, Schön, Thomas B.

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  3. 3
    On robust input design for nonlinear dynamical models

    On robust input design for nonlinear dynamical models by Valenzuela, Patricio E., Dahlin, Johan, Rojas, Cristian R., Schön, Thomas B.

    Published in Automatica (Oxford)
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  4. 4
    Particle Metropolis–Hastings using gradient and Hessian information

    Particle Metropolis–Hastings using gradient and Hessian information by Dahlin, Johan, Lindsten, Fredrik, Schön, Thomas B.

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  5. 5
    Detecting and positioning overtaking vehicles using 1D optical flow

    Detecting and positioning overtaking vehicles using 1D optical flow by Hultqvist, Daniel, Roll, Jacob, Svensson, Fredrik, Dahlin, Johan, Schon, Thomas B.

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  6. 6
    Particle metropolis hastings using Langevin dynamics

    Particle metropolis hastings using Langevin dynamics by Dahlin, Johan, Lindsten, Fredrik, Schon, Thomas B.

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  7. 7
    Sparse Bayesian ARX models with flexible noise distributions

    Sparse Bayesian ARX models with flexible noise distributions by Dahlin, Johan, Wills, Adrian, Ninness, Brett

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  8. 8
    Constructing Metropolis-Hastings proposals using damped BFGS updates⁎⁎This work was supported by the Australian Research Council Discovery Project DP140104350

    Constructing Metropolis-Hastings proposals using damped BFGS updates⁎⁎This work was supported by the Australian Research Council Discovery Project DP140104350 by Dahlin, Johan, Wills, Adrian, Ninness, Brett

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  9. 9
    Constructing Metropolis-Hastings proposals using damped BFGS updates

    Constructing Metropolis-Hastings proposals using damped BFGS updates by Dahlin, Johan, Wills, Adrian, Ninness, Brett

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  10. 10
    Sparse Bayesian ARX models with flexible noise distributions⁎⁎This work was supported by the Australian Research Council Discovery Project DP140104350. The EEG data was kindly provided by Eline Borch Petersen and Thomas Lunner at Eriksholm Research Centre, Oticon A/S, Denmark

    Sparse Bayesian ARX models with flexible noise distributions⁎⁎This work was supported by the Australian Research Council Discovery Project DP140104350. The EEG data was kindly prov... by Dahlin, Johan, Wills, Adrian, Ninness, Brett

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  11. 11
    Particle filter-based Gaussian process optimisation for parameter inference

    Particle filter-based Gaussian process optimisation for parameter inference by Dahlin, Johan, Lindsten, Fredrik

    Published in arXiv.org
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  12. 12
    Quasi-Newton particle Metropolis-HastingsE-mail address to corresponding author: johan.dahlin@liu.se. This work was supported by: Learning of complex dynamical systems (Contract num. 637-2014-466), Probabilistic modelling of dynamical systems (Contract num. 621-2013-5524) and CADICS, a Linnaeus Center, all funded by the Swedish Research Council

    Quasi-Newton particle Metropolis-HastingsE-mail address to corresponding author: johan.dahlin@liu.se. This work was supported by: Learning of complex dynamical systems (Contract nu... by Dahlin, Johan, Lindsten, Fredrik, B. Schön, Thomas

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  13. 13
    Accurate Gaussian Mixture Model Smoothing using a Two-Filter Approach

    Accurate Gaussian Mixture Model Smoothing using a Two-Filter Approach by Balenzuela, Mark P., Dahlin, Johan, Bartlett, Nathan, Wills, Adrian G., Renton, Christopher, Ninness, Brett

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  14. 14
    Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models

    Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models by Dahlin, Johan, Schön, Thomas B

    Published in arXiv.org
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  15. 15
    Practical Bayesian System Identification using Hamiltonian Monte Carlo

    Practical Bayesian System Identification using Hamiltonian Monte Carlo by Hendriks, Johannes, Wills, Adrian, Ninness, Brett, Dahlin, Johan

    Published in arXiv.org
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  16. 16
    Marginalizing Gaussian Process Hyperparameters using Sequential Monte Carlo

    Marginalizing Gaussian Process Hyperparameters using Sequential Monte Carlo by Svensson, Andreas, Dahlin, Johan, Schön, Thomas B

    Published in arXiv.org
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  17. 17
    Quasi-Newton particle Metropolis-Hastings

    Quasi-Newton particle Metropolis-Hastings by Dahlin, Johan, Lindsten, Fredrik, Schön, Thomas B

    Published in arXiv.org
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  18. 18
    Newton-based maximum likelihood estimation in nonlinear state space modelsE-mail address to corresponding author: manon.kok@liu.se. This work is supported by CADICS, a Linnaeus Center, and by the project Probabilistic modeling of dynamical systems (Contract number: 621-2013-5524), both funded by the Swedish Research Council (VR)

    Newton-based maximum likelihood estimation in nonlinear state space modelsE-mail address to corresponding author: manon.kok@liu.se. This work is supported by CADICS, a Linnaeus Cen... by Kok, Manon, Dahlin, Johan, Schön, Thomas B., Wills, Adrian

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  19. 19
    Particle Metropolis-Hastings using gradient and Hessian information

    Particle Metropolis-Hastings using gradient and Hessian information by Dahlin, Johan, Lindsten, Fredrik, Schön, Thomas B

    Published in arXiv.org
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  20. 20
    Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals

    Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals by Dahlin, Johan, Wills, Adrian, Ninness, Brett

    Published in arXiv.org
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