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We establish structure learning consistency of our algorithms in the large-sample limit, and empirically validate our methods individually and collectively through extensive numerical comparisons. The combined merits of pPC and PATH achieve significant computational reductions compared to the PC algorithm without sacrificing the accuracy of estimated structures, and our generally applicable HGI strategy reliably improves the estimation structural accuracy of popular hybrid algorithms with negligible additional computational expense. Our empirical results demonstrate the competitive empirical performance of pHGS against many state-of-the-art structure learning algorithms.<\/jats:p>","DOI":"10.1007\/s10994-022-06145-4","type":"journal-article","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T16:15:43Z","timestamp":1647447343000},"page":"1695-1738","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Partitioned hybrid learning of Bayesian network structures"],"prefix":"10.1007","volume":"111","author":[{"given":"Jireh","family":"Huang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2100-8840","authenticated-orcid":false,"given":"Qing","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,16]]},"reference":[{"issue":"6","key":"6145_CR1","doi-asserted-by":"publisher","first-page":"716","DOI":"10.1109\/tac.1974.1100705","volume":"19","author":"H Akaike","year":"1974","unstructured":"Akaike, H. 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