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Modelling regimes with Bayesian network mixtures
Linköpings universitet, Institutionen för datavetenskap, Databas och informationsteknik. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-8678-1164
Linköpings universitet, Institutionen för datavetenskap, Statistik. Linköpings universitet, Tekniska fakulteten.
2017 (engelsk)Inngår i: Proceedings of the 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden / [ed] Niklas Lavesson, Linköping: Linköping University Electronic Press, 2017, Vol. 137, s. 20-29, artikkel-id 002Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2017. Vol. 137, s. 20-29, artikkel-id 002
Serie
Linköping Electronic Conference Proceedings, ISSN 1650-3686, E-ISSN 1650-3740 ; 137
Emneord [en]
Bayesian networks, hidden Markov models, regimes, algorithmic trading
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-137664ISBN: 9789176854969 (tryckt)OAI: oai:DiVA.org:liu-137664DiVA, id: diva2:1098364
Konferanse
The 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017, May 15–16, 2017, Karlskrona, Sweden
Tilgjengelig fra: 2017-05-24 Laget: 2017-05-24 Sist oppdatert: 2019-07-04bibliografisk kontrollert

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Modelling regimes with Bayesian network mixtures(542 kB)4 nedlastinger
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