资 源 简 介
Dynamic Bayesian networks (DBN) are widely applied in modeling various biological networks, including the gene regulatory network. Due to several NP-hardness results on learning static Bayesian network, most methods for learning DBN are heuristic, that employ either local search such as greedy hill-climbing, or a meta optimization framework such as genetic algorithm or simulated annealing.
We present GlobalMIT, a toolbox for learning the globally optimal DBN structure using a recently introduced information theoretic based scoring metric named mutual information test (MIT). Under MIT, learning the globally optimal DBN can be efficiently achieved in polynomial time. The toolbox is implemented in Matlab, with also a C++ stand-alone implementation of the search engine for improved performance.
This research work was part of a larger project (BF040037) funded by Australia-India Strategic Research Fund. The chief investigators were A/Pro
