Dynamic Bayesian network

A dynamic Bayesian network is a Bayesian network that represents sequences of variables. These sequences are often time-series (for example, in speech recognition) or sequences of symbols (for example, protein sequences). The hidden Markov model can be considered as a simple dynamic Bayesian network.

See also

Recursive Bayesian estimation

References

• Learning Dynamic Bayesian Networks (1997), Zoubin Ghahramani, Lecture Notes In Computer Science, Vol. 1387, 168-197
• [1] Friedman, N., Murphy, K., and Russell, S. (1998). Learning the structure of dynamic probabilistic networks. In UAI’98, pages 139–147. Morgan Kaufmann.

Software

• BNT, 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 - Modeling gene regulatory network via global optimization of dynamic bayesian network (released under a GPL license)