Infinite dynamic bayesian networks
Name
Tenenbaum-2011-Infinite dynamic.pdf
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711.69 KB
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Author(s)
Doshi-Velez, Finale P.
Wingate, David
Tenenbaum, Joshua B.
Roy, Nicholas
Date Issued
June 2011
Journal
Proceedings of the 28th International Conference on Machine Learning (ICML 2011)
Publisher
International Machine Learning Society
Citation
Doshi-Velez, Finale, David Wingate, Joshua Tenenbaum and Nicholas Roy. "Infinite Dynamic Bayesian Networks." The 28th International Conference on Machine Learning, Bellevue, WA, USA, June 28-July 2, 2011,
Version
Author's final manuscript
Abstract
We present the infinite dynamic Bayesian network model (iDBN), a nonparametric, factored state-space model that generalizes dynamic Bayesian networks (DBNs). The iDBN can infer every aspect of a DBN: the number of hidden factors, the number of values each factor can take, and (arbitrarily complex) connections and conditionals between factors and observations. In this way, the iDBN generalizes other nonparametric state space models, which until now generally
focused on binary hidden nodes and more restricted connection structures. We show how
this new prior allows us to find interesting structure in benchmark tests and on two realworld datasets involving weather data and neural information flow networks.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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