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The prediction model of dynamic Bayesian network structure learning algorithm was improved, integrated into the Gibbs sampling algorithm model prediction, joined the predicted relationship between different factors affecting the node, is given based on the variable relationship between the dynamic Bayesian network structure design, using a moment on the different nodes and state influence factors to predict the probability distribution of the moment state nodes. The experimental results show that the model is simple in structure, more accurate than the traditional learning method of Bayesian network structure, and more practical.<\/jats:p>","DOI":"10.3233\/jcm-204330","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T14:19:34Z","timestamp":1587478774000},"page":"41-48","source":"Crossref","is-referenced-by-count":1,"title":["Dynamic Bayesian network state prediction based on variable relationship"],"prefix":"10.1177","volume":"21","author":[{"given":"Zhongzhi","family":"Liao","sequence":"first","affiliation":[{"name":"School of Information Engineering, Wuhan University of Technology, Wuhan, Hubei, China"},{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China"},{"name":"Jiangsu Agricultural Internet of Things Perception and System Control Engineering Laboratory, Nanjing, Jiangsu, China"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China"}]},{"given":"Jianxin","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electronics Information Engineering, Nantong Vocational University, Nantong, Jiangsu, China"}]},{"given":"Junjie","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China"},{"name":"Jiangsu Agricultural Internet of Things Perception and System Control Engineering Laboratory, Nanjing, Jiangsu, China"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JCM-204330_ref1","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1016\/j.eswa.2014.06.037","article-title":"A method for root cause analysis with a bayesian belief network and fuzzy cognitive map","volume":"42","author":"Wee","year":"2015","journal-title":"Expert Systems with Applications"},{"issue":"C","key":"10.3233\/JCM-204330_ref2","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.jisa.2016.02.001","article-title":"A taxonomy for attack graph generation and usage in network security","volume":"29","author":"Kaynar","year":"2016","journal-title":"Journal of Information Security and Applications"},{"key":"10.3233\/JCM-204330_ref3","unstructured":"X.Y. 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