References$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_upper_j_idt159",{id:"formSmash:upper:j_idt159",widgetVar:"widget_formSmash_upper_j_idt159",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:upper:referencesLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_upper_j_idt165_j_idt167",{id:"formSmash:upper:j_idt165:j_idt167",widgetVar:"widget_formSmash_upper_j_idt165_j_idt167",target:"formSmash:upper:j_idt165:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});

Inference in Temporal 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)Doctoral thesis, comprehensive summary (Other academic)
##### Abstract [en]

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

KTH Royal Institute of Technology, 2016. , 16 p.
##### Series

TRITA-MAT-A, 2016:08
##### National Category

Probability Theory and Statistics
##### Research subject

Applied and Computational Mathematics
##### Identifiers

URN: urn:nbn:se:kth:diva-193934ISBN: 978-91-7729-115-2OAI: oai:DiVA.org:kth-193934DiVA: diva2:1034699
##### Public defence

2016-10-21, F3, Lindstedtsvagen, Stockholm, 13:00
##### Opponent

PrimeFaces.cw("AccordionPanel","widget_formSmash_j_idt434",{id:"formSmash:j_idt434",widgetVar:"widget_formSmash_j_idt434",multiple:true});
##### Supervisors

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#####

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##### Note

##### List of papers

This thesis develops mathematical tools used to model and forecast different economic phenomena. The primary starting point is the temporal graphical model. Four main topics, all with applications in finance, are studied.

The first two papers develop inference methods for networks of continuous time Markov processes, so called Continuous Time Bayesian Networks. Methodology for learning the structure of the network and for doing inference and simulation is developed. Further, models are developed for high frequency foreign exchange data.

The third paper models growth of gross domestic product (GDP) which is observed at a very low frequency. This application is special and has several difficulties which are dealt with in a novel way using a framework developed in the paper. The framework is motivated using a temporal graphical model. The method is evaluated on US GDP growth with good results.

The fourth paper study inference in dynamic Bayesian networks using Monte Carlo methods. A new method for sampling random variables is proposed. The method divides the sample space into subspaces. This allows the sampling to be done in parallel with independent and distinct sampling methods on the subspaces. The methodology is demonstrated on a volatility model for stock prices and some toy examples with promising results.

The fifth paper develops an algorithm for learning the full distribution in a harness race, a ranked event. It is demonstrated that the proposed methodology outperforms logistic regression which is the main competitor. It also outperforms the market odds in terms of accuracy.

QC 20161013

Available from: 2016-10-13 Created: 2016-10-12 Last updated: 2016-10-13Bibliographically approved1. Testing for Causality in Continuous time Bayesian Network Models of High-Frequency Data$(function(){PrimeFaces.cw("OverlayPanel","overlay787940",{id:"formSmash:j_idt489:0:j_idt496",widgetVar:"overlay787940",target:"formSmash:j_idt489:0:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

2. Structure Learning and Mixed Radix representation in Continuous Time Bayesian Networks$(function(){PrimeFaces.cw("OverlayPanel","overlay1034595",{id:"formSmash:j_idt489:1:j_idt496",widgetVar:"overlay1034595",target:"formSmash:j_idt489:1:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

3. Nowcasting with dynamic masking$(function(){PrimeFaces.cw("OverlayPanel","overlay1034583",{id:"formSmash:j_idt489:2:j_idt496",widgetVar:"overlay1034583",target:"formSmash:j_idt489:2:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

4. Decomposition Sampling Applied to Parallelization of Metropolis-Hastings$(function(){PrimeFaces.cw("OverlayPanel","overlay787938",{id:"formSmash:j_idt489:3:j_idt496",widgetVar:"overlay787938",target:"formSmash:j_idt489:3:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

5. Forecasting Ranking in Harness Racing using Probabilities Induced by Expected Positions$(function(){PrimeFaces.cw("OverlayPanel","overlay1034587",{id:"formSmash:j_idt489:4:j_idt496",widgetVar:"overlay1034587",target:"formSmash:j_idt489:4:partsLink",showEvent:"mousedown",hideEvent:"mousedown",showEffect:"blind",hideEffect:"fade",appendToBody:true});});

References$(function(){PrimeFaces.cw("TieredMenu","widget_formSmash_lower_j_idt1617",{id:"formSmash:lower:j_idt1617",widgetVar:"widget_formSmash_lower_j_idt1617",autoDisplay:true,overlay:true,my:"left top",at:"left bottom",trigger:"formSmash:lower:referencesLink",triggerEvent:"click"});}); $(function(){PrimeFaces.cw("OverlayPanel","widget_formSmash_lower_j_idt1619_j_idt1622",{id:"formSmash:lower:j_idt1619:j_idt1622",widgetVar:"widget_formSmash_lower_j_idt1619_j_idt1622",target:"formSmash:lower:j_idt1619:permLink",showEffect:"blind",hideEffect:"fade",my:"right top",at:"right bottom",showCloseIcon:true});});