Dynamic Bayesian Model

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A Dynamic Bayesian Model is a directed conditional statistical metamodel that can represent a Markov chain.



  • (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Dynamic_Bayesian_network Retrieved:2015-10-1.
    • 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.








  • (Kanazawa et al., 1995) ⇒ K. Kanazawa, Daphne Koller, and S. Russell. (1995). “Stochastic Simulation Algorithms for Dynamic Probabilistic Networks.” In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence.