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Probabilistic Graphical Models and Their Inferences (Tutorial)
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.ORCID iD: 0000-0003-1654-9148
2019 (English)In: 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) / [ed] Robert Birke and Ingrid Nunes, 2019, p. 251-252Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

Probabilistic graphical models are useful for mod- elling stochastic phenomena for doing inferences and reasoning under uncertainty. Especially, chain graph models and Bayesian networks can be used as probabilistic expert systems where inferences can be done with junction tree algorithm, etc. And they can be extended to capture multi-stage decision contexts. Fundamentally these models capture (in)dependence structure of the context, but model learning is hard in practice. There are methods to do this, from simple independence test-based ones to more advanced score-based methods. When these models are used as classifiers, model learning can be done discriminatively, thus resulting higher classification accuracies in them.

Place, publisher, year, edition, pages
2019. p. 251-252
Keywords [en]
(in)dependence, efficient, prediction
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-162374DOI: 10.1109/FAS-W.2019.00067ISBN: 978-1-7281-2406-3 (electronic)OAI: oai:DiVA.org:umu-162374DiVA, id: diva2:1343534
Conference
2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W), Umeå, Sweden, 16-20 June, 2019
Available from: 2019-08-16 Created: 2019-08-16 Last updated: 2019-08-20Bibliographically approved

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Wijayatunga, Priyantha

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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf