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Schön, Thomas B., Professororcid.org/0000-0001-5183-234X

Open this publication in new window or tab >>Data consistency approach to model validation### Lindholm, Andreas

### Zachariah, Dave

### Stoica, Peter

### Schön, Thomas B.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_0_j_idt188_some",{id:"formSmash:j_idt184:0:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_0_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_0_j_idt188_otherAuthors",{id:"formSmash:j_idt184:0:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_0_j_idt188_otherAuthors",multiple:true}); 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 ()
#####

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Available from: 2019-05-07 Created: 2019-06-24 Last updated: 2019-06-26Bibliographically approved

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.

Open this publication in new window or tab >>Deep convolutional networks in system identification### Andersson, Carl

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Horta Ribeiro, Antônio

### Tiels, Koen

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Wahlström, Niklas

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_1_j_idt188_some",{id:"formSmash:j_idt184:1:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_1_j_idt188_some",multiple:true}); ### Schön, Thomas B.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_1_j_idt188_otherAuthors",{id:"formSmash:j_idt184:1:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_1_j_idt188_otherAuthors",multiple:true}); Show others...PrimeFaces.cw("SelectBooleanButton","widget_formSmash_j_idt184_1_j_idt188_j_idt202",{id:"formSmash:j_idt184:1:j_idt188:j_idt202",widgetVar:"widget_formSmash_j_idt184_1_j_idt188_j_idt202",onLabel:"Hide others...",offLabel:"Show others..."}); 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
#####

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##### Note

to appear

Available from: 2019-11-21 Created: 2019-11-21 Last updated: 2019-11-21Bibliographically approvedOpen this publication in new window or tab >>Deep kernel learning for integral measurements### Jidling, Carl

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Hendriks, Johannes

### Schön, Thomas B.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Wills, Adrian

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_2_j_idt188_some",{id:"formSmash:j_idt184:2:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_2_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_2_j_idt188_otherAuthors",{id:"formSmash:j_idt184:2:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_2_j_idt188_otherAuthors",multiple:true}); 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)
#####

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Available from: 2019-09-04 Created: 2019-10-02 Last updated: 2019-10-02Bibliographically approved

Open this publication in new window or tab >>Elements of Sequential Monte Carlo### Naesseth, Christian A.

### Lindsten, Fredrik

### Schön, Thomas B.

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_3_j_idt188_some",{id:"formSmash:j_idt184:3:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_3_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_3_j_idt188_otherAuthors",{id:"formSmash:j_idt184:3:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_3_j_idt188_otherAuthors",multiple:true}); 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]

##### 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 ()
#####

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Available from: 2019-12-20 Created: 2019-12-20 Last updated: 2019-12-20Bibliographically approved

Columbia Univ, New York, NY 10027 USA.

Linkoping Univ, Linkoping, Sweden.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.

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.

Open this publication in new window or tab >>Evaluating model calibration in classification### Vaicenavicius, Juozas

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Widmann, David

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Andersson, Carl

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Lindsten, Fredrik

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_4_j_idt188_some",{id:"formSmash:j_idt184:4:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_4_j_idt188_some",multiple:true}); ### Roll, Jacob

### Schön, Thomas B.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_4_j_idt188_otherAuthors",{id:"formSmash:j_idt184:4:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_4_j_idt188_otherAuthors",multiple:true}); Show others...PrimeFaces.cw("SelectBooleanButton","widget_formSmash_j_idt184_4_j_idt188_j_idt202",{id:"formSmash:j_idt184:4:j_idt188:j_idt202",widgetVar:"widget_formSmash_j_idt184_4_j_idt188_j_idt202",onLabel:"Hide others...",offLabel:"Show others..."}); 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
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Available from: 2019-04-25 Created: 2019-11-21 Last updated: 2019-11-21Bibliographically approved

Open this publication in new window or tab >>Getting started with particle Metropolis-Hastings for inference in nonlinear dynamical models### Dahlin, Johan

### Schön, Thomas B.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_5_j_idt188_some",{id:"formSmash:j_idt184:5:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_5_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_5_j_idt188_otherAuthors",{id:"formSmash:j_idt184:5:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_5_j_idt188_otherAuthors",multiple:true}); 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]

##### 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 ()
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##### 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

Department of Computer and Information Science, Linköping University, Linköping, Sweden.

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.

Open this publication in new window or tab >>High-Dimensional Filtering Using Nested Sequential Monte Carlo### Naesseth, Christian A.

### Lindsten, Fredrik

### Schön, Thomas B.

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_6_j_idt188_some",{id:"formSmash:j_idt184:6:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_6_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_6_j_idt188_otherAuthors",{id:"formSmash:j_idt184:6:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_6_j_idt188_otherAuthors",multiple:true}); 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]

##### 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 ()
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##### 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

Linkoping Univ, Div Stat & Machine Learning, S-58183 Linkoping, Sweden.

Linkoping Univ, Div Stat & Machine Learning, S-58183 Linkoping, Sweden.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.

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.

Open this publication in new window or tab >>Neutron transmission strain tomography for non-constant stress-free lattice spacing### Hendriks, Johannes N.

### Jidling, Carl

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Schön, Thomas B.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Wills, Adrian

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_7_j_idt188_some",{id:"formSmash:j_idt184:7:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_7_j_idt188_some",multiple:true}); ### Wensrich, Christopher M.

### Kisi, Erich H.

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_7_j_idt188_otherAuthors",{id:"formSmash:j_idt184:7:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_7_j_idt188_otherAuthors",multiple:true}); Show others...PrimeFaces.cw("SelectBooleanButton","widget_formSmash_j_idt184_7_j_idt188_j_idt202",{id:"formSmash:j_idt184:7:j_idt188:j_idt202",widgetVar:"widget_formSmash_j_idt184_7_j_idt188_j_idt202",onLabel:"Hide others...",offLabel:"Show others..."}); 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 ()
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Available from: 2019-07-11 Created: 2019-09-26 Last updated: 2019-10-02Bibliographically approved

Open this publication in new window or tab >>On model order priors for Bayesian identification of SISO linear systems### Valenzuela, Patricio E.

### Schön, Thomas B.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.### Rojas, Cristian R.

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_8_j_idt188_some",{id:"formSmash:j_idt184:8:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_8_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_8_j_idt188_otherAuthors",{id:"formSmash:j_idt184:8:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_8_j_idt188_otherAuthors",multiple:true}); 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]

##### 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 ()
#####

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##### 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

KTH.

KTH.

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.

Open this publication in new window or tab >>Optimal controller/observer gains of discounted-cost LQG systems### Bijl, Hildo

### Schön, Thomas B.

Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_9_j_idt188_some",{id:"formSmash:j_idt184:9:j_idt188:some",widgetVar:"widget_formSmash_j_idt184_9_j_idt188_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt184_9_j_idt188_otherAuthors",{id:"formSmash:j_idt184:9:j_idt188:otherAuthors",widgetVar:"widget_formSmash_j_idt184_9_j_idt188_otherAuthors",multiple:true}); 2019 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 101, p. 471-474Article in journal (Refereed) Published
##### Abstract [en]

##### 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 ()
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##### 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

Delft University of Technology.

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.