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Schön, Thomas B., ProfessorORCID iD iconorcid.org/0000-0001-5183-234X
Publications (10 of 57) Show all publications
Lindholm, A., Zachariah, D., Stoica, P. & Schön, T. B. (2019). Data consistency approach to model validation. IEEE Access, 7, 59788-59796
Open this publication in new window or tab >>Data consistency approach to model validation
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 59788-59796Article in journal (Refereed) Published
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-386361 (URN)10.1109/ACCESS.2019.2915109 (DOI)000468615500001 ()
Available from: 2019-05-07 Created: 2019-06-24 Last updated: 2019-06-26Bibliographically approved
Andersson, C., Horta Ribeiro, A., Tiels, K., Wahlström, N. & Schön, T. B. (2019). Deep convolutional networks in system identification. In: Proc. 58th Conference on Decision and Control: . Paper presented at CDC 2019, December 11–13, Nice, France. IEEE
Open this publication in new window or tab >>Deep convolutional networks in system identification
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2019 (English)In: Proc. 58th Conference on Decision and Control, IEEE, 2019Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2019
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-397528 (URN)
Conference
CDC 2019, December 11–13, Nice, France
Note

to appear

Available from: 2019-11-21 Created: 2019-11-21 Last updated: 2019-11-21Bibliographically approved
Jidling, C., Hendriks, J., Schön, T. B. & Wills, A. (2019). Deep kernel learning for integral measurements. Computing Research Repository (1909.01844)
Open this publication in new window or tab >>Deep kernel learning for integral measurements
2019 (English)In: Computing Research Repository, no 1909.01844Article in journal (Other academic) Submitted
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-394088 (URN)
Available from: 2019-09-04 Created: 2019-10-02 Last updated: 2019-10-02Bibliographically approved
Naesseth, C. A., Lindsten, F. & Schön, T. B. (2019). Elements of Sequential Monte Carlo. FOUNDATIONS AND TRENDS IN MACHINE LEARNING, 12(3), 187-306
Open this publication in new window or tab >>Elements of Sequential Monte Carlo
2019 (English)In: FOUNDATIONS AND TRENDS IN MACHINE LEARNING, ISSN 1935-8237, Vol. 12, no 3, p. 187-306Article in journal (Refereed) Published
Abstract [en]

A core problem in statistics and probabilistic machine learning is to compute probability distributions and expectations. This is the fundamental problem of Bayesian statistics and machine learning, which frames all inference as expectations with respect to the posterior distribution. The key challenge is to approximate these intractable expectations. In this tutorial, we review sequential Monte Carlo (SMC), a random-sampling-based class of methods for approximate inference. First, we explain the basics of SMC, discuss practical issues, and review theoretical results. We then examine two of the main user design choices: the proposal distributions and the so called intermediate target distributions. We review recent results on how variational inference and amortization can be used to learn efficient proposals and target distributions. Next, we discuss the SMC estimate of the normalizing constant, how this can be used for pseudo-marginal inference and inference evaluation. Throughout the tutorial we illustrate the use of SMC on various models commonly used in machine learning, such as stochastic recurrent neural networks, probabilistic graphical models, and probabilistic programs.

Place, publisher, year, edition, pages
NOW PUBLISHERS INC, 2019
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-400416 (URN)10.1561/2200000074 (DOI)000500235400001 ()
Available from: 2019-12-20 Created: 2019-12-20 Last updated: 2019-12-20Bibliographically approved
Vaicenavicius, J., Widmann, D., Andersson, C., Lindsten, F., Roll, J. & Schön, T. B. (2019). Evaluating model calibration in classification. In: 22nd International Conference on Artificial Intelligence and Statistics: . Paper presented at AISTATS 2019, April 16–18, Naha, Japan (pp. 3459-3467).
Open this publication in new window or tab >>Evaluating model calibration in classification
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2019 (English)In: 22nd International Conference on Artificial Intelligence and Statistics, 2019, p. 3459-3467Conference paper, Published paper (Refereed)
Series
Proceedings of Machine Learning Research, ISSN 2640-3498 ; 89
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-397519 (URN)
Conference
AISTATS 2019, April 16–18, Naha, Japan
Available from: 2019-04-25 Created: 2019-11-21 Last updated: 2019-11-21Bibliographically approved
Dahlin, J. & Schön, T. B. (2019). Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models. Journal of Statistical Software, 88(CN2), 1-41
Open this publication in new window or tab >>Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models
2019 (English)In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 88, no CN2, p. 1-41Article in journal (Refereed) Published
Abstract [en]

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 implementation in the statistical programming language R. We employ a step-by-step approach to develop an implementation of the PMH algorithm (and the particle filter within) together with the reader. This final implementation is also available as the package pmhtutorial in the CRAN repository. Throughout the tutorial, we provide some intuition as to how the algorithm operates and discuss some solutions to problems that might occur in practice. To illustrate the use of PMH, we consider parameter inference in a linear Gaussian state-space model with synthetic data and a nonlinear stochastic volatility model with real-world data.

Keywords
Bayesian inference, state-space models, particle filtering, particle Markov chain Monte Carlo, stochastic volatility model
National Category
Probability Theory and Statistics Control Engineering
Identifiers
urn:nbn:se:uu:diva-368618 (URN)10.1807/jss.v088.c02 (DOI)000463413300001 ()
Funder
Swedish Foundation for Strategic Research , RIT15-0012Swedish Research Council, 621-2013-5524
Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-04-30Bibliographically approved
Naesseth, C. A., Lindsten, F. & Schön, T. B. (2019). High-Dimensional Filtering Using Nested Sequential Monte Carlo. IEEE Transactions on Signal Processing, 67(16), 4177-4188
Open this publication in new window or tab >>High-Dimensional Filtering Using Nested Sequential Monte Carlo
2019 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 67, no 16, p. 4177-4188Article in journal (Refereed) Published
Abstract [en]

Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering. However, SMC without a good proposal distribution can perform poorly, in particular in high dimensions. We propose nested sequential Monte Carlo, a methodology that generalizes the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correctSMCalgorithm. This way, we can compute an "exact approximation" of, e. g., the locally optimal proposal, and extend the class of models forwhichwe can perform efficient inference using SMC. We showimproved accuracy over other state-of-the-art methods on several spatio-temporal state-space models.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2019
Keywords
Particle filtering, spatio-temporal models, state space models, approximate Bayesian inference, backward simulation
National Category
Signal Processing Probability Theory and Statistics
Identifiers
urn:nbn:se:uu:diva-391279 (URN)10.1109/TSP.2019.2926035 (DOI)000476798500004 ()
Funder
Swedish Research Council, 2016-04278Swedish Research Council, 621-2016-06079Swedish Foundation for Strategic Research , RIT15-0012Swedish Foundation for Strategic Research , ICA16-0015
Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-22Bibliographically approved
Hendriks, J. N., Jidling, C., Schön, T. B., Wills, A., Wensrich, C. M. & Kisi, E. H. (2019). Neutron transmission strain tomography for non-constant stress-free lattice spacing. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 456, 64-73
Open this publication in new window or tab >>Neutron transmission strain tomography for non-constant stress-free lattice spacing
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2019 (English)In: Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, ISSN 0168-583X, E-ISSN 1872-9584, Vol. 456, p. 64-73Article in journal (Refereed) Published
National Category
Probability Theory and Statistics Applied Mechanics
Identifiers
urn:nbn:se:uu:diva-393639 (URN)10.1016/j.nimb.2019.07.005 (DOI)000480669600013 ()
Available from: 2019-07-11 Created: 2019-09-26 Last updated: 2019-10-02Bibliographically approved
Valenzuela, P. E., Schön, T. B. & Rojas, C. R. (2019). On model order priors for Bayesian identification of SISO linear systems. International Journal of Control, 92(7), 1645-1661
Open this publication in new window or tab >>On model order priors for Bayesian identification of SISO linear systems
2019 (English)In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820, Vol. 92, no 7, p. 1645-1661Article in journal (Refereed) Published
Abstract [en]

A method for the identification of single input single output linear systems is presented. The method employs a Bayesian approach to compute the posterior distribution of the model parameters given the data-set. Since this distribution is often unavailable in closed form, a Metropolis Hastings algorithm is implemented to draw samples from it. To implement the sampler, the inclusion of prior information regarding the model order of the identified system is discussed. As one of the main contributions of this work, a prior over the Hankel singular values of the model is imposed. Numerical examples illustrate the method.

Keywords
System identification, Bayesian estimation, Metropolis Hastings sampler, model order prior
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-368619 (URN)10.1080/00207179.2017.1406147 (DOI)000472558100017 ()
Funder
Swedish Research Council, 2016-06079Swedish Foundation for Strategic Research , RIT15-0012Swedish Research Council, 621-2015-4393Swedish Research Council, 621-2013-5524Swedish Research Council, 621-2011-5890Swedish Research Council, 621-2009-4017
Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-08-15Bibliographically approved
Bijl, H. & Schön, T. B. (2019). Optimal controller/observer gains of discounted-cost LQG systems. Automatica, 101, 471-474
Open this publication in new window or tab >>Optimal controller/observer gains of discounted-cost LQG systems
2019 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 101, p. 471-474Article in journal (Refereed) Published
Abstract [en]

The linear-quadratic-Gaussian (LQG) control paradigm is well-known in literature. The strategy of minimizing the cost function is available, both for the case where the state is known and where it is estimated through an observer. The situation is different when the cost function has an exponential discount factor, also known as a prescribed degree of stability. In this case, the optimal control strategy is only available when the state is known. This paper builds onward from that result, deriving an optimal control strategy when working with an estimated state. Expressions for the resulting optimal expected cost are also given. 

Place, publisher, year, edition, pages
Elsevier, 2019
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-368617 (URN)10.1016/j.automatica.2018.12.040 (DOI)000458941700052 ()
Funder
Swedish Research Council, 621-2016-06079Swedish Foundation for Strategic Research , RIT15-0012
Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-03-11Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5183-234X

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