A dynamic Bayesian network (DBN) is a
Bayesian network (BN) which relates variables to each other over adjacent time steps.
History
A dynamic Bayesian network (DBN) 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 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]
Friedman, N.; Murphy, K.; Russell, S. (1998). Learning the structure of dynamic probabilistic networks. UAI’98. Morgan Kaufmann. pp. 139–147.
CiteSeerX10.1.1.75.2969.
bnt on
GitHub: the Bayes Net Toolbox for Matlab, by Kevin Murphy, (released under a
GPL license)
Graphical Models Toolkit (GMTK): an open-source, publicly available toolkit for rapidly prototyping statistical models using dynamic graphical models (DGMs) and dynamic Bayesian networks (DBNs). GMTK can be used for applications and research in speech and language processing, bioinformatics, activity recognition, and any time-series application.
DBmcmc : Inferring Dynamic Bayesian Networks with MCMC, for Matlab (free software)
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)
aGrUM: C++ library (with Python bindings) for different types of PGMs including Bayesian Networks and Dynamic Bayesian Networks (released under the GPLv3)
FALCON: Matlab toolbox for contextualization of DBNs models of regulatory networks with biological quantitative data, including various regularization schemes to model prior biological knowledge (released under the GPLv3)