Dynamic Bayesian network

A Dynamic Bayesian Network (DBN) is a Bayesian network which relates variables to each other over adjacent time steps. This is often called a Two-Timeslice BN (2TBN) because it says that at any point in time T, the value of a variable can be calculated from the internal regressors and the immediate prior value (time T-1). DBNs were developed by Paul Dagum in the early 1990s when he led research funded by two National Science Foundation grants at Stanford University's Section on Medical Informatics.[1][2] Dagum developed DBNs to unify and extend traditional linear state-space models such as Kalman filters, linear and normal forecasting models such as ARMA and simple dependency models such as hidden Markov models into a general probabilistic representation and inference mechanism for arbitrary nonlinear and non-normal time-dependent domains.[3][4]

Today, DBNs are common in robotics, and have shown potential for a wide range of data mining applications. For example, they have been used in speech recognition, digital forensics, protein sequencing, and bioinformatics. DBN is a generalization of hidden Markov models and Kalman filters.[5]

See also

References

  1. Paul Dagum; Adam Galper; Eric Horvitz (1992). "Dynamic Network Models for Forecasting". Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence (AUAI Press): 41-48.
  2. Paul Dagum; Adam Galper; Eric Horvitz; Adam Seiver (1995). "Uncertain Reasoning and Forecasting". International Journal of Forecasting 11(1): 73-87.
  3. >Paul Dagum; Adam Galper; Eric Horvitz (June 1991). "Temporal Probabilistic Reasoning: Dynamic Network Models for Forecasting". Knowledge Systems Laboratory. Section on Medical Informatics, Stanford University.
  4. >Paul Dagum; Adam Galper; Eric Horvitz (1993). "Forecasting Sleep Apnea with Dynamic Network Models". Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (AUAI Press): 64-71.
  5. Stuart Russell; Peter Norvig (2010). Artificial Intelligence: A Modern Approach (PDF) (Third ed.). Prentice Hall. p. 566. ISBN 978-0136042594. Retrieved 22 October 2014. dynamic Bayesian networks (which include hidden Markov models and Kalman filters as special cases)

Software


This article is issued from Wikipedia - version of the Thursday, March 03, 2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.