Direct calculation inference algorithm for discrete dynamic Bayesian network

Discrete dynamic Bayesian network is a capable tool for modeling and quality inferring for dynamic process, but the current inference algorithms we can read in all materials are all based on complicated figure transformations. They are hard to programming and need long time for calculation. Aimed on this problem, we present a direct calculation inference algorithm for discrete dynamic Bayesian network based on the probability theory and the basic characters of the Bayesian network and verify it by samples. The most advantage of this algorithm is that it needs not performing complicated figure transformation, it is easy to programming, and under some conditions, it can work out the results quickly and directly. It is more useful in some applications where the time requests is relaxed.