References$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_upper_j_idt146",{id:"formSmash:upper:j_idt146",widgetVar:"widget_formSmash_upper_j_idt146",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:upper:referencesLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_upper_j_idt147_j_idt149",{id:"formSmash:upper:j_idt147:j_idt149",widgetVar:"widget_formSmash_upper_j_idt147_j_idt149",target:"formSmash:upper:j_idt147:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});

Particle filters and Markov chains for learning of dynamical systemsPrimeFaces.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});
function selectAll()
{
var panelSome = $(PrimeFaces.escapeClientId("formSmash:some"));
var panelAll = $(PrimeFaces.escapeClientId("formSmash:all"));
panelAll.toggle();
toggleList(panelSome.get(0).childNodes, panelAll);
toggleList(panelAll.get(0).childNodes, panelAll);
}
/*Toggling the list of authorPanel nodes according to the toggling of the closeable second panel */
function toggleList(childList, panel)
{
var panelWasOpen = (panel.get(0).style.display == 'none');
// console.log('panel was open ' + panelWasOpen);
for (var c = 0; c < childList.length; c++) {
if (childList[c].classList.contains('authorPanel')) {
clickNode(panelWasOpen, childList[c]);
}
}
}
/*nodes have styleClass ui-corner-top if they are expanded and ui-corner-all if they are collapsed */
function clickNode(collapse, child)
{
if (collapse && child.classList.contains('ui-corner-top')) {
// console.log('collapse');
child.click();
}
if (!collapse && child.classList.contains('ui-corner-all')) {
// console.log('expand');
child.click();
}
}
PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2013 (English)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

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

Linköping University Electronic Press, 2013. , 42 p.
##### Series

Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1530
##### Keyword [en]

Bayesian learning, System identification, Sequential Monte Carlo, Markov chain Monte Carlo, Particle MCMC, Particle filters, Particle smoothers
##### National Category

Control Engineering Probability Theory and Statistics
##### Identifiers

URN: urn:nbn:se:liu:diva-97692DOI: 10.3384/diss.diva-97692ISBN: 978-91-7519-559-9 (print)OAI: oai:DiVA.org:liu-97692DiVA: diva2:654644
##### Public defence

2013-10-25, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
##### Opponent

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt376",{id:"formSmash:j_idt376",widgetVar:"widget_formSmash_j_idt376",multiple:true});
##### Supervisors

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt382",{id:"formSmash:j_idt382",widgetVar:"widget_formSmash_j_idt382",multiple:true});
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt388",{id:"formSmash:j_idt388",widgetVar:"widget_formSmash_j_idt388",multiple:true});
##### Projects

CNDMCADICS
##### Funder

Swedish Research Council
Available from: 2013-10-08 Created: 2013-09-19 Last updated: 2013-10-08Bibliographically approved
##### List of papers

Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods provide computational tools for systematic inference and learning in complex dynamical systems, such as nonlinear and non-Gaussian state-space models. This thesis builds upon several methodological advances within these classes of Monte Carlo methods.Particular emphasis is placed on the combination of SMC and MCMC in so called particle MCMC algorithms. These algorithms rely on SMC for generating samples from the often highly autocorrelated state-trajectory. A specific particle MCMC algorithm, referred to as particle Gibbs with ancestor sampling (PGAS), is suggested. By making use of backward sampling ideas, albeit implemented in a forward-only fashion, PGAS enjoys good mixing even when using seemingly few particles in the underlying SMC sampler. This results in a computationally competitive particle MCMC algorithm. As illustrated in this thesis, PGAS is a useful tool for both Bayesian and frequentistic parameter inference as well as for state smoothing. The PGAS sampler is successfully applied to the classical problem of Wiener system identification, and it is also used for inference in the challenging class of non-Markovian latent variable models.Many nonlinear models encountered in practice contain some tractable substructure. As a second problem considered in this thesis, we develop Monte Carlo methods capable of exploiting such substructures to obtain more accurate estimators than what is provided otherwise. For the filtering problem, this can be done by using the well known Rao-Blackwellized particle filter (RBPF). The RBPF is analysed in terms of asymptotic variance, resulting in an expression for the performance gain offered by Rao-Blackwellization. Furthermore, a Rao-Blackwellized particle smoother is derived, capable of addressing the smoothing problem in so called mixed linear/nonlinear state-space models. The idea of Rao-Blackwellization is also used to develop an online algorithm for Bayesian parameter inference in nonlinear state-space models with affine parameter dependencies.

1. Backward simulation methods for Monte Carlo statistical inference$(function(){PrimeFaces.cw("OverlayPanel","overlay654562",{id:"formSmash:j_idt424:0:j_idt428",widgetVar:"overlay654562",target:"formSmash:j_idt424:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Ancestor Sampling for Particle Gibbs$(function(){PrimeFaces.cw("OverlayPanel","overlay605133",{id:"formSmash:j_idt424:1:j_idt428",widgetVar:"overlay605133",target:"formSmash:j_idt424:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. Bayesian semiparametric Wiener system identification$(function(){PrimeFaces.cw("OverlayPanel","overlay641716",{id:"formSmash:j_idt424:2:j_idt428",widgetVar:"overlay641716",target:"formSmash:j_idt424:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

4. An Efficient Stochastic Approximation EM Algorithm using Conditional Particle Filters$(function(){PrimeFaces.cw("OverlayPanel","overlay624904",{id:"formSmash:j_idt424:3:j_idt428",widgetVar:"overlay624904",target:"formSmash:j_idt424:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

5. Rao-Blackwellized Particle Smoothers for Mixed Linear/Nonlinear State-Space Models$(function(){PrimeFaces.cw("OverlayPanel","overlay624911",{id:"formSmash:j_idt424:4:j_idt428",widgetVar:"overlay624911",target:"formSmash:j_idt424:4:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

6. A non-degenerate Rao-Blackwellised particle filter for estimating static parameters in dynamical models$(function(){PrimeFaces.cw("OverlayPanel","overlay551267",{id:"formSmash:j_idt424:5:j_idt428",widgetVar:"overlay551267",target:"formSmash:j_idt424:5:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

7. An Explicit Variance Reduction Expression for the Rao-Blackwellised Particle Filter$(function(){PrimeFaces.cw("OverlayPanel","overlay551250",{id:"formSmash:j_idt424:6:j_idt428",widgetVar:"overlay551250",target:"formSmash:j_idt424:6:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

References$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_lower_j_idt1133",{id:"formSmash:lower:j_idt1133",widgetVar:"widget_formSmash_lower_j_idt1133",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:lower:referencesLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_lower_j_idt1134_j_idt1136",{id:"formSmash:lower:j_idt1134:j_idt1136",widgetVar:"widget_formSmash_lower_j_idt1134_j_idt1136",target:"formSmash:lower:j_idt1134:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});