{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:34:52Z","timestamp":1760243692502,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,9,24]],"date-time":"2022-09-24T00:00:00Z","timestamp":1663977600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171360"],"award-info":[{"award-number":["62171360"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>The Bayesian Network (BN) structure learning algorithm based on dynamic programming can obtain global optimal solutions. However, when the sample cannot fully contain the information of the real structure, especially when the sample size is small, the obtained structure is inaccurate. Therefore, this paper studies the planning mode and connotation of dynamic programming, restricts its process with edge and path constraints, and proposes a dynamic programming BN structure learning algorithm with double constraints under small sample conditions. The algorithm uses double constraints to limit the planning process of dynamic programming and reduces the planning space. Then, it uses double constraints to limit the selection of the optimal parent node to ensure that the optimal structure conforms to prior knowledge. Finally, the integrating prior-knowledge method and the non-integrating prior-knowledge method are simulated and compared. The simulation results verify the effectiveness of the method proposed and prove that the integrating prior knowledge can significantly improve the efficiency and accuracy of BN structure learning.<\/jats:p>","DOI":"10.3390\/e24101354","type":"journal-article","created":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T23:13:27Z","timestamp":1664147607000},"page":"1354","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Programming BN Structure Learning Algorithm Integrating Double Constraints under Small Sample Condition"],"prefix":"10.3390","volume":"24","author":[{"given":"Zhigang","family":"Lv","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"},{"name":"School of Electronic Information Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}]},{"given":"Yiwei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}]},{"given":"Ruohai","family":"Di","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}]},{"given":"Hongxi","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}]},{"given":"Xiaojing","family":"Sun","sequence":"additional","affiliation":[{"name":"General Office, Northwest Institute of Mechanical and Electrical Engineering, Xianyang 712099, China"}]},{"given":"Chuchao","family":"He","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0194-8226","authenticated-orcid":false,"given":"Xiaoyan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pearl, J. 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