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Bayesian structure learning in graphical modelsPrimeFaces.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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PrimeFaces.cw("AccordionPanel","widget_formSmash_responsibleOrgs",{id:"formSmash:responsibleOrgs",widgetVar:"widget_formSmash_responsibleOrgs",multiple:true}); 2016 (English)Licentiate thesis, comprehensive summary (Other academic)
##### Abstract [en]

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

Stockholm: KTH Royal Institute of Technology, 2016. , p. viii, 19
##### Series

TRITA-MAT-A ; 2015:16
##### Keyword [en]

Bayesian statistics, graphical models, Bayesian networks, Markov networks, structure learning
##### National Category

Probability Theory and Statistics
##### Research subject

Applied and Computational Mathematics
##### Identifiers

URN: urn:nbn:se:kth:diva-179852ISBN: 978-91-7595-832-3 (print)OAI: oai:DiVA.org:kth-179852DiVA, id: diva2:892063
##### Presentation

2016-01-28, Rum 3418, Instititionen för matematik, Lindstedtsvägen 25, Kungliga Tekniska Högskolan, Stockholm, 14:00 (English)
##### Opponent

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##### Supervisors

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#####

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##### Note

##### List of papers

This thesis consists of two papers studying structure learning in probabilistic graphical models for both undirected graphs anddirected acyclic graphs (DAGs).

Paper A, presents a novel family of graph theoretical algorithms, called the junction tree expanders, that incrementally construct junction trees for decomposable graphs. Due to its Markovian property, the junction tree expanders are shown to be suitable for proposal kernels in a sequential Monte Carlo (SMC) sampling scheme for approximating a graph posterior distribution. A simulation study is performed for the case of Gaussian decomposable graphical models showing efficiency of the suggested unified approach for both structural and parametric Bayesian inference.

Paper B, develops a novel prior distribution over DAGs with the ability to express prior knowledge in terms of graph layerings. In conjunction with the prior, a search and score algorithm based on the layering property of DAGs, is developed for performing structure learning in Bayesian networks. A simulation study shows that the search and score algorithm along with the prior has superior performance for learning graph with a clearly layered structure compared with other priors.

QC 20160111

Available from: 2016-01-11 Created: 2016-01-04 Last updated: 2016-01-11Bibliographically approved1. Bayesian structure learning in graphical models using sequential Monte Carlo$(function(){PrimeFaces.cw("OverlayPanel","overlay892652",{id:"formSmash:j_idt519:0:j_idt523",widgetVar:"overlay892652",target:"formSmash:j_idt519:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. The Minimal Hoppe-Beta Prior Distribution for Directed Acyclic Graphs and Structure Learning$(function(){PrimeFaces.cw("OverlayPanel","overlay892655",{id:"formSmash:j_idt519:1:j_idt523",widgetVar:"overlay892655",target:"formSmash:j_idt519:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

isbn
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