Open Access
Volume 39, Number 6, December 2021
Page(s) 1356 - 1367
Published online 21 March 2022
  1. Wittig F, Jameson A. Exploiting qualitative knowledge in the learning of conditional probabilities of Bayesian networks[C]//Proceedings of the Sixteenth International Conference on Uncertainty in Artificial Intelligence, California, 2000 [Google Scholar]
  2. Feelders A, Gaag L. Learning Bayesian network parameters under order constraints[J]. International Journal of Approximate Reasoning, 2006, 42(1/2) : 37–53 [Article] [Google Scholar]
  3. Masegosa A R, Feelders A J, Gaag L C V D. Learning from incomplete data in Bayesian networks with qualitative influences[J]. International Journal of Approximate Reasoning, 2016, 69 : 18–34. 10.1016/j.ijar.2015.11.004 [Google Scholar]
  4. Martin Plajner, Jirí Vomlel. Learning bipartite Bayesian networks under monotonicity restrictions[J]. International Journal of General Systems, 2020, 49(1) : 88–111 10.1080/03081079.2019.1692004 [Google Scholar]
  5. Zhou Y, Fenton N, Zhu C. An empirical study of Bayesian network parameter learning with monotonic influence constraints[J]. Decision Support Systems, 2016, 87 : 69–79 10.1016/j.dss.2016.05.001 [Google Scholar]
  6. Di Ruohai, Gao Xiaoguang, Guo Zhigao. Parameter learning of discrete Bayesian networks based on monotonicity constraints[J]. Systems Engineering and Electronics, 2014, 18(5) : 272–277 [Article] (in Chinese) [Google Scholar]
  7. Di Ruohai, Gao Xiaoguang, Guo Zhigao. Learning Bayesian networks parameters under new monotonic constraints[J]. Journal of Systems Engineering and Electronics, 2017, 28(6) : 1248–1255 10.21629/JSEE.2017.06.22 [CrossRef] [Google Scholar]
  8. Ren Jia, Gao Xiaoguang, Bai Yong. Discrete DBN parameter learning under the condition of incomplete information with small samples[J]. Systems Engineering and Electronics, 2012, 34(8) : 1723–1728 [Article] (in Chinese) [Google Scholar]
  9. Zeng Qiang, Huang Zheng, Wei Shuhuan. Bayesian network parameter learning method based on expert prior knowledge and monotonicity constraint[J]. Systems Engineering and Electronics, 2020, 42(3) : 642–656 [Article] (in Chinese) [Google Scholar]
  10. Niculescu R, Mitchell T, Rao B. Bayesian network learning with parameter constraints[J]. Journal of Machine Learning Research, 2006, 7(1) : 1357–1383 [Google Scholar]
  11. Campos C, Ji Q. Improving Bayesian network parameter learning using constraints[C]//Proceedings of the Nineteenth International Conference on Pattern Recognition in Florida, 2008 [Google Scholar]
  12. Liao Wenhui, Ji Qiang. Learning Bayesian network parameters under incomplete data with domain knowledge[J]. Pattern Recognition, 2009, 42(11) : 3046–3056 10.1016/j.patcog.2009.04.006 [Google Scholar]
  13. Zhou Y, Fenton F, Neil M. Bayesian network approach to multinomial parameter learning using data and expert judgments[J]. International Journal of Approximate Reasoning, 2014, 55(5) : 1252–1268 10.1016/j.ijar.2014.02.008 [Google Scholar]
  14. Zhou Y, Fenton N, Hospadales T, et al. Probabilistic graphical models parameter learning with transferred prior and constraints[C]//Proceedings of the Thirty First International Conference on Uncertainty in Artificial Intelligence, 2015 [Google Scholar]
  15. Guo Zhigao, Gao Xiaoguang, Di Ruohai. BN parameter learning based on double constraints under the condition of small dataset[J]. Acta Automatica Sinica, 2014, 40(7) : 1509–1516 [Article] (in Chinese) [Google Scholar]
  16. Rui Chang, Shoemaker R, Wei Wang. A novel knowledge-driven systems biology approach for phenotype prediction upon genetic intervention[J]. IEEE/ACM Transactions on Computational Bioinformatics, 2011, 3(8) : 683–697 [Article] [Google Scholar]
  17. Guo Z G, Gao X G, Ren H, et al. Learning Bayesian network parameters from small data sets: a further constrained qualitatively maximum a posteriori method[J]. International Journal of Approximate Reasoning, 2017, 91(12)22–35 [Google Scholar]
  18. Heckerman D, Wellman M P. Bayesian networks[J]. Communications of ACM, 1995, 38(3): 27–30 10.1145/203330.203336 [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.