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Modelling regimes with Bayesian network mixtures
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-8678-1164
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Science & Engineering.
2017 (English)In: Proceedings of the Thirtieth Annual Workshop of the Swedish Artificial Intelligence Society, 2017, 20-29 p.Conference paper, Published paper (Refereed)
Abstract [en]

Bayesian networks (BNs) are advantageous when representing single independence models, however they do not allow us to model changes among the relationships of the random variables over time. Due to such regime changes, it may be necessary to use different BNs at different times in order to have an appropriate model over the random variables. In this paper we propose two extensions to the traditional hidden Markov model, allowing us to represent both the different regimes using different BNs, and potential driving forces behind the regime changes, by modelling potential dependence between state transitions and some observable variables. We show how expectation maximisation can be used to learn the parameters of the proposed model, and run both synthetic and real-world experiments to show the model’s potential.

Place, publisher, year, edition, pages
2017. 20-29 p.
Keyword [en]
Bayesian networks, hidden Markov models, regimes, algorithmic trading
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-137664OAI: oai:DiVA.org:liu-137664DiVA: diva2:1098364
Conference
The Thirtieth Annual Workshop of the Swedish Artificial Intelligence Society - SAIS 2017
Available from: 2017-05-24 Created: 2017-05-24 Last updated: 2017-06-02Bibliographically approved

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