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Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solutionPrimeFaces.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)In: Mechanical systems and signal processing, ISSN 0888-3270, E-ISSN 1096-1216, Vol. 104, p. 915-928Article in journal (Refereed) Published
##### Abstract [en]

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

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD , 2018. Vol. 104, p. 915-928
##### Keywords [en]

Probabilistic modelling, Bayesian methods, Nonlinear system identification, Sequential Monte Carlo, Particle filter, Approximate Bayesian computations, Highly informative observations, Tempering, Wiener-Hammerstein model
##### National Category

Probability Theory and Statistics
##### Identifiers

URN: urn:nbn:se:uu:diva-350269DOI: 10.1016/j.ymssp.2017.09.016ISI: 000423652800057OAI: oai:DiVA.org:uu-350269DiVA, id: diva2:1206003
#####

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##### Funder

Swedish Research Council, 621-2013-5524, 2016-04278, 621-2016-06079Swedish Foundation for Strategic Research , RIT15-0012Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-08-21Bibliographically approved
##### In thesis

Probabilistic (or Bayesian) modeling and learning offers interesting possibilities for systematic representation of uncertainty using probability theory. However, probabilistic learning often leads to computationally challenging problems. Some problems of this type that were previously intractable can now be solved on standard personal computers thanks to recent advances in Monte Carlo methods. In particular, for learning of unknown parameters in nonlinear state-space models, methods based on the particle filter (a Monte Carlo method) have proven very useful. A notoriously challenging problem, however, still occurs when the observations in the state-space model are highly informative, i.e. when there is very little or no measurement noise present, relative to the amount of process noise. The particle filter will then struggle in estimating one of the basic components for probabilistic learning, namely the likelihood p(datalparameters). To this end we suggest an algorithm which initially assumes that there is substantial amount of artificial measurement noise present. The variance of this noise is sequentially decreased in an adaptive fashion such that we, in the end, recover the original problem or possibly a very close approximation of it. The main component in our algorithm is a sequential Monte Carlo (SMC) sampler, which gives our proposed method a clear resemblance to the SMC2 method. Another natural link is also made to the ideas underlying the approximate Bayesian computation (ABC). We illustrate it with numerical examples, and in particular show promising results for a challenging Wiener-Hammerstein benchmark problem.

1. Machine learning with state-space models, Gaussian processes and Monte Carlo methods$(function(){PrimeFaces.cw("OverlayPanel","overlay1240418",{id:"formSmash:j_idt771:0:j_idt775",widgetVar:"overlay1240418",target:"formSmash:j_idt771:0:parentLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

doi
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