分段平稳变结构DBN模型区域内的结构学习

为解决在有限的样本数据和缺乏先验知识条件下对非平稳随机过程进行建模的问题，提出分段平稳变结构动态贝叶斯网络(dynamic Bayesian network，DBN)的概念。在每一个平稳模型区域内，将模型近似表征为一阶条件独立DBN，稀疏的结构加快了DBN的学习过程。改进了基于标准马尔可夫链蒙特卡罗(Markov chain Monte Carlo，MCMC)的DBN结构学习算法，利用自适应增加的马尔可夫链个数,有效防止标准MCMC算法在寻优迭代计算中出现过早收敛。与标准MCMC算法、结构期望最大化算法等进行对比实验，验证了所提算法的有效性。

Structure learning for piecewise stationary varying DBN in model section

To learn the dynamic Bayesian network(DBN) structure under the limited sample data capacity and prior assumptions,the modeling approach for piecewise stationary and varying DBN in non-stationary stochastic process is studied.In the model section,the approximate representing by a first-order conditional independent DBN is utilized,which makes the model topology parse and leads to fast learning.An improved Markov chain Monte Carlo(MCMC) optimization algorithm for DBN structure learning is proposed,which avoids the pre-convergence in classical MCMC algorithm via increasing the Markov chain number adaptively.Comparison experimental results illustrate that the presented algorithm is more effective than classical MCMC or structural expectation maximization methods.