Advertisement

Dynamic and Temporal Bayesian Networks

  • Luis Enrique SucarEmail author
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Dynamic Bayesian network models extend BNs to represent the temporal evolution of a certain process. There are two basic types of Bayesian network models for dynamic processes: state based and event based. Dynamic Bayesian networks are state-based models that represent the state of each variable at discrete time intervals. Event-based models represent the changes in the state of each state variable; each temporal variable will then correspond to the time in which a state change occurs. In this chapter, we will review dynamic Bayesian networks and event networks, including representation, inference, and learning. The chapter includes two application examples: dynamic Bayesian networks for gesture recognition and temporal nodes Bayesian networks for HIV mutational pathways prediction.

Keywords

Human Immunodeficiency Virus Bayesian Network Gaussian Mixture Model Gesture Recognition Dilate Pupil 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Arroyo-Figueroa, G., Sucar, L.E.: A temporal Bayesian network for diagnosis and prediction. In: Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI). Morgan-Kaufmann, San Mateo, pp. 13–20 (1999)Google Scholar
  2. 2.
    Avilés-Arriaga, H.H., Sucar, L.E., Mendoza-Durán, C.E., Pineda-Cortés, L.A.: Comparison of dynamic naive Bayesian classifiers and hidden Markov models for gesture recognition. J. Appl. Res. Technol. 9(1), 81–102 (2011)Google Scholar
  3. 3.
    Cooper, G.F., Herskovitz, E.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9(4), 309–348 (1992)zbMATHGoogle Scholar
  4. 4.
    Friedman, N., Murphy, K., Russell, S.: Learning the Structure of Dynamic Probabilistic Networks. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial (UAI). Morgan Kaufmann Publishers Inc., pp. 139–147 (1998)Google Scholar
  5. 5.
    Galán, S.F., Arroyo-Figueroa, G., Díez, F.J., Sucar, L.E.: Comparison of two types of event Bayesian networks: a case study. Appl. Artif. Intell. 21(3), 185–209 (2007)CrossRefGoogle Scholar
  6. 6.
    Ghahramani, Z.: Learning Dynamic Bayesian Networks. Lecture Notes in Computer Science 1387, 168–197 (1998)Google Scholar
  7. 7.
    Hernández-Leal, P., González, J.A., Morales, E.F., Sucar, L.E.: Learning temporal nodes Bayesian networks. Int. J. Approx. Reason. 54(8), 956–977 (2013)CrossRefGoogle Scholar
  8. 8.
    Hernández-Leal, P., Rios-Flores, A., Ávila-Rios, S., Reyes-Terán, G., González, J.A., Fiedler-Cameras, L., Orihuela-Espina, F., Morales, E.F., Sucar, L.E.: Discovering HIV mutational pathways using temporal bayesian networks. Artif. Intell. Med. 57(3), 185–195 (2013)CrossRefGoogle Scholar
  9. 9.
    Murphy, K.: Dynamic Bayesian networks: representation, inference and learning. dissertation, University of California, Berkeley (2002)Google Scholar
  10. 10.
    Rabiner, R., Juang, B.: Fundamentals of Speech Recognition. Prentice Hall, New Jersey (1993)Google Scholar
  11. 11.
    Shafer, R.: Rationale and uses of a public HIV drug-resistance database. J. Infect. Dis. 194(1), 51–58 (2006)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)Santa María TonantzintlaMexico

Personalised recommendations