Dynamic Bayesian network

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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 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.[1]

See also[edit]


  1. ^ Stuart Russell; Peter Norvig (2010). Artificial Intelligence: A Modern Approach (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) 


  • BNT at Google Code: the Bayes Net Toolbox for Matlab, by Kevin Murphy, (released under a GPL license)
  • DBmcmc : Inferring Dynamic Bayesian Networks with MCMC, for Matlab (free software)
  • GlobalMIT Matlab toolbox at Google Code: Modeling gene regulatory network via global optimization of dynamic bayesian network (released under a GPL license)
  • libDAI: C++ library that provides implementations of various (approximate) inference methods for discrete graphical models; supports arbitrary factor graphs with discrete variables, including discrete Markov Random Fields and Bayesian Networks (released under the FreeBSD license)