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Intrinsic Learning of Dynamic Bayesian Networks

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Book cover PRICAI 2014: Trends in Artificial Intelligence (PRICAI 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8862))

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Abstract

Programs that learn Bayesian networks normally learn directed acyclic graphs (DAGs) of arbitrary structure, including those with repeating structures, such as dynamic Bayesian networks (DBNs). Perhaps for that reason there is relatively little literature on learning DBNs specifically and more focusing on applying general learners to the task. Here we modify a general causal discovery program to search specifically for dynamic Bayesian networks, and we identify the benefits in the quality of the models discovered and the time taken to discover them.

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, Monash University, Clayton, Victoria, Australia

    Alex Black, Kevin B. Korb & Ann E. Nicholson

Authors
  1. Alex Black
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  2. Kevin B. Korb
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  3. Ann E. Nicholson
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Editor information

Editors and Affiliations

  1. MIMOS Berhad Technology Park Malaysia, 57000, Bukit Jalil, KL, Malaysia

    Duc-Nghia Pham

  2. Kyungpook National University, Sankyuk-Dong, Buk-Gu, 702-701, Daegu, Korea

    Seong-Bae Park

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Black, A., Korb, K.B., Nicholson, A.E. (2014). Intrinsic Learning of Dynamic Bayesian Networks. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_21

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  • DOI: https://doi.org/10.1007/978-3-319-13560-1_21

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13559-5

  • Online ISBN: 978-3-319-13560-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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