uu.seUppsala University Publications

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Machine learning with state-space models, Gaussian processes and Monte Carlo methodsPrimeFaces.cw("AccordionPanel","widget_formSmash_some",{id:"formSmash:some",widgetVar:"widget_formSmash_some",multiple:true}); PrimeFaces.cw("AccordionPanel","widget_formSmash_all",{id:"formSmash:all",widgetVar:"widget_formSmash_all",multiple:true});
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2018 (English)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

##### Place, publisher, year, edition, pages

Uppsala: Acta Universitatis Upsaliensis, 2018. , p. 74
##### Series

Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1709
##### Keywords [en]

Machine learning, State-space models, Gaussian processes
##### National Category

Signal Processing Probability Theory and Statistics
##### Research subject

Electrical Engineering with specialization in Automatic Control
##### Identifiers

URN: urn:nbn:se:uu:diva-357611ISBN: 978-91-513-0417-5 (print)OAI: oai:DiVA.org:uu-357611DiVA, id: diva2:1240418
##### Public defence

2018-10-12, ITC 2446, Lägerhyddsvägen 2, Uppsala, 10:15 (English)
##### Opponent

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##### Supervisors

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#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt472",{id:"formSmash:j_idt472",widgetVar:"widget_formSmash_j_idt472",multiple:true}); Available from: 2018-09-18 Created: 2018-08-21 Last updated: 2018-10-02
##### List of papers

Numbers are present everywhere, and when they are collected and recorded we refer to them as data. Machine learning is the science of learning mathematical models from data. Such models, once learned from data, can be used to draw conclusions, understand behavior, predict future evolution, and make decisions. This thesis is mainly concerned with two particular statistical models for this purpose: the state-space model and the Gaussian process model, as well as a combination thereof. To learn these models from data, Monte Carlo methods are used, and in particular sequential Monte Carlo (SMC) or particle filters.

The thesis starts with an introductory background on state-space models, Gaussian processes and Monte Carlo methods. The main contribution lies in seven scientific papers. Several contributions are made on the topic of learning nonlinear state-space models with the use of SMC. An existing SMC method is tailored for learning in state-space models with little or no measurement noise. The SMC-based method particle Gibbs with ancestor sampling (PGAS) is used for learning an approximation of the Gaussian process state-space model. PGAS is also combined with stochastic approximation expectation maximization (EM). This method, which we refer to as particle stochastic approximation EM, is a general method for learning parameters in nonlinear state-space models. It is later applied to the particular problem of maximum likelihood estimation in jump Markov linear models. An alternative and non-standard approach for how to use SMC to estimate parameters in nonlinear state-space models is also presented.

There are also two contributions not related to learning state-space models. One is how SMC can be used also for learning hyperparameters in Gaussian process regression models. The second is a method for assessing consistency between model and data. By using the model to simulate new data, and compare how similar that data is to the observed one, a general criterion is obtained which follows directly from the model specification. All methods are implemented and illustrated, and several are also applied to various real-world examples.

1. A flexible state–space model for learning nonlinear dynamical systems$(function(){PrimeFaces.cw("OverlayPanel","overlay1060732",{id:"formSmash:j_idt539:0:j_idt545",widgetVar:"overlay1060732",target:"formSmash:j_idt539:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Data Consistency Approach to Model Validation$(function(){PrimeFaces.cw("OverlayPanel","overlay1239823",{id:"formSmash:j_idt539:1:j_idt545",widgetVar:"overlay1239823",target:"formSmash:j_idt539:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. Learning dynamical systems with particle stochastic approximation EM$(function(){PrimeFaces.cw("OverlayPanel","overlay1239526",{id:"formSmash:j_idt539:2:j_idt545",widgetVar:"overlay1239526",target:"formSmash:j_idt539:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

4. Identification of jump Markov linear models using particle filters$(function(){PrimeFaces.cw("OverlayPanel","overlay756540",{id:"formSmash:j_idt539:3:j_idt545",widgetVar:"overlay756540",target:"formSmash:j_idt539:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

5. Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution$(function(){PrimeFaces.cw("OverlayPanel","overlay1206003",{id:"formSmash:j_idt539:4:j_idt545",widgetVar:"overlay1206003",target:"formSmash:j_idt539:4:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

6. Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations$(function(){PrimeFaces.cw("OverlayPanel","overlay1239520",{id:"formSmash:j_idt539:5:j_idt545",widgetVar:"overlay1239520",target:"formSmash:j_idt539:5:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

7. Marginalizing Gaussian process hyperparameters using sequential Monte Carlo$(function(){PrimeFaces.cw("OverlayPanel","overlay866347",{id:"formSmash:j_idt539:6:j_idt545",widgetVar:"overlay866347",target:"formSmash:j_idt539:6:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

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