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Learning non-stationary dynamic bayesian networks

Publication ,  Journal Article
Robinson, JW; Hartemink, AJ
Published in: Journal of Machine Learning Research
December 1, 2010

Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional DBN structure learning is that the data are generated by a stationary process, an assumption that is not true in many important settings. In this paper, we introduce a new class of graphical model called a nonstationary dynamic Bayesian network, in which the conditional dependence structure of the underlying data-generation process is permitted to change over time. Non-stationary dynamic Bayesian networks represent a new framework for studying problems in which the structure of a network is evolving over time. Some examples of evolving networks are transcriptional regulatory networks during an organism's development, neural pathways during learning, and traffic patterns during the day. We define the non-stationary DBN model, present an MCMC sampling algorithm for learning the structure of the model from time-series data under different assumptions, and demonstrate the effectiveness of the algorithm on both simulated and biological data. © 2010 Joshua W. Robinson and Alexander J. Hartemink.

Duke Scholars

Published In

Journal of Machine Learning Research

EISSN

1533-7928

ISSN

1532-4435

Publication Date

December 1, 2010

Volume

11

Start / End Page

3647 / 3680

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4905 Statistics
  • 4611 Machine learning
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Robinson, J. W., & Hartemink, A. J. (2010). Learning non-stationary dynamic bayesian networks. Journal of Machine Learning Research, 11, 3647–3680.
Robinson, J. W., and A. J. Hartemink. “Learning non-stationary dynamic bayesian networks.” Journal of Machine Learning Research 11 (December 1, 2010): 3647–80.
Robinson JW, Hartemink AJ. Learning non-stationary dynamic bayesian networks. Journal of Machine Learning Research. 2010 Dec 1;11:3647–80.
Robinson, J. W., and A. J. Hartemink. “Learning non-stationary dynamic bayesian networks.” Journal of Machine Learning Research, vol. 11, Dec. 2010, pp. 3647–80.
Robinson JW, Hartemink AJ. Learning non-stationary dynamic bayesian networks. Journal of Machine Learning Research. 2010 Dec 1;11:3647–3680.

Published In

Journal of Machine Learning Research

EISSN

1533-7928

ISSN

1532-4435

Publication Date

December 1, 2010

Volume

11

Start / End Page

3647 / 3680

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4905 Statistics
  • 4611 Machine learning
  • 17 Psychology and Cognitive Sciences
  • 08 Information and Computing Sciences