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 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 have been shown to produce equivalent solutions to Hidden Markov Models and Kalman Filters.
- Murphy, Kevin (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. UC Berkeley, Computer Science Division.
- Ghahramani, Zoubin (1997). "Learning Dynamic Bayesian Networks". Lecture Notes In Computer Science 1387: 168–197. CiteSeerX: 10.1.1.56.7874.
- Friedman, N.; Murphy, K.; Russell, S. (1998). "Learning the structure of dynamic probabilistic networks". UAI’98. Morgan Kaufmann. pp. 139–147. CiteSeerX: 10.1.1.75.2969.
- 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)
|This statistics-related article is a stub. You can help Wikipedia by expanding it.|