Abstract
Dynamic Bayesian networks (DBNs) are increasingly adopted as tools for the modeling of dynamic domains involving uncertainty. Due to their ease of modeling, repetitive DBNs have become the standard. However, repetition does not allow the independence relations to vary over time. Non-repetitive DBNs do allow for modeling time-varying relations, but are hard to apply to dynamic domains.
This paper presents a novel method that facilitates the use of non-repetitive DBNs and simplifies learning DBNs in general. This is achieved by learning disjoint sets of independence relations of separate parts of a DBN, and, subsequently, joining these relations together to obtain the complete set of independence relations of the DBN. Our simplified learning method improves previous methods by removing redundant operations which yields computational savings in the learning process of the network. Experimental results show that the simplified learning method facilitates the use of non-repetitive DNBs and enables us to build them in a seamless fashion.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Castillo, E., Gutiérrez, J.M., Hadi, A.S.: Expert Systems and Probabilistic Network Models. Springer, New York (1997)
Deviren, M., Daoudi, K.: Continuous speech recognition using dynamic Bayesian networks: a fast decoding algorithm. In: Proc PGM 2002, Spain, pp. 54–60 (2002)
Flesch, I., Lucas, P.J.F.: Independence Decomposition in Dynamic Bayesian Networks. In: Proc. ECSQARU, pp. 560–571 (2007)
Jensen, F.V., Nielsen, T.D.: Bayesian Networks and Decision Graphs. Springer, Heidelberg (2007)
Cowell, R.G., Philip Dawid, A., Lauritzen, S.L., Spiegelhalter, D.J.: Probabilistic Networks and Expert Systems. Springer, New York (1999)
Murphy, K.P.: Dynamic Bayesian Networks: Representation, Inference and Learning. PhD Thesis, UC Berkeley (2002)
Kjaerulff, U.: A computational scheme for reasoning in dynamic probabilistic networks. In: Proc. UAI 1992, pp. 121–129 (1992)
Tucker, A., Liu, X.: Learning Dynamic Bayesian Networks from Multivariate Time Series with Changing Dependencies. IDA (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Flesch, I., Postma, E.O. (2009). Simplifying Learning in Non-repetitive Dynamic Bayesian Networks. In: Sossai, C., Chemello, G. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2009. Lecture Notes in Computer Science(), vol 5590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02906-6_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-02906-6_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02905-9
Online ISBN: 978-3-642-02906-6
eBook Packages: Computer ScienceComputer Science (R0)