广义隐马尔可夫模型的快速前向后向算法

动态贝叶斯网络是解决非线性动态系统不确定性推理问题的一个重要工具。通过对改进前向后向算法计算方式的改变，提出了一种快速前向后向算法。不仅从理论上推导了快速前向算法、快速后向算法，并且将这两种算法结合推导出快速前向后向算法。由复杂度分析可知，提出算法的复杂度较低，仿真实验验证了快速推理算法的正确性和推理的高效性。

Fast forwards-backwards algorithm of generalized hidden Markov model

Dynamic Bayesian networks are an important tool for the nonlinear dynamical systems with uncertainty inference. A fast forwards-backwards algorithom is proposed by introducing a new computation method into the improved forwards-backwards (IFB) algorithom. The fast forwards algorithm and backwards algorithm are deduced in theory, and the two algorithms are combined to deduce the fast forwards-backwards algorithm. According to the complexity analysis, it’s easy to see that the complexity of the proposed algorithm is lower. It is proved by the simulation experiments that the algorithm is correct and efficient.