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});});

Structure Learning of Bayesian Networks with Bounded Treewidth Using Mixed Integer Linear ProgrammingPrimeFaces.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}); 2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
##### Abstract [en]

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

2014.
##### Series

TRITA-MAT-E, 2014:45
##### National Category

Discrete Mathematics
##### Identifiers

URN: urn:nbn:se:kth:diva-148972OAI: oai:DiVA.org:kth-148972DiVA: diva2:742119
##### Subject / course

Mathematics
##### Educational program

Master of Science - Mathematics
#####

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt381",{id:"formSmash:j_idt381",widgetVar:"widget_formSmash_j_idt381",multiple:true});
##### Supervisors

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt387",{id:"formSmash:j_idt387",widgetVar:"widget_formSmash_j_idt387",multiple:true});
##### Examiners

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt393",{id:"formSmash:j_idt393",widgetVar:"widget_formSmash_j_idt393",multiple:true});
Available from: 2014-08-31 Created: 2014-08-16 Last updated: 2014-08-31Bibliographically approved

Strukturinlärning av Bayesianska nätverk av begränsad trävidd med hjälp av heltalsprogrammering (Swedish)

When given a Bayesian network, a common use of it is calculating conditional probabilities. This is known as inference. In order to be able to infer effectively, the structure of the Bayesian network is required to have low treewidth. Therefore, the problem of learning the structure of Bayesian networks with bounded treewidth is studied in this thesis. This is solved by reducing the problem to a mixed integer linear problem using several formulation for the structure of the Bayesian network as well as for bounding the treewidth of the structure. Solving the problem in this way gives an algorithm known as an anytime algorithm which can be aborted during the run and return a solution as well as an upper bound for the value of the best possible solution. Tests show that several of these formulations are of practical use as implementations of them prove solutions to be optimal or nearly optimal for several data sets.

References$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_lower_j_idt1101",{id:"formSmash:lower:j_idt1101",widgetVar:"widget_formSmash_lower_j_idt1101",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:lower:referencesLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_lower_j_idt1102_j_idt1104",{id:"formSmash:lower:j_idt1102:j_idt1104",widgetVar:"widget_formSmash_lower_j_idt1102_j_idt1104",target:"formSmash:lower:j_idt1102:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});