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Kitson, N. K., Constantinou, A., Guo, Z., Liu, Y., and Chobtham, K. (2021). A survey of Bayesian network structure learning. arXiv:2109.11415 [cs.LG]
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Kitson, N. K., & Constantinou, A. (2021). Learning Bayesian networks from demographic and health survey data. Journal of Biomedical Informatics, Vol. 113, Article 103588. [Open-Access DOI].
Constantinou, A. C. (2021). The importance of temporal information in Bayesian network structure learning. Expert Systems with Applications, Vol. 164, Article 113814. [Open-access DOI]
Yang, L., Constantinou, A. C., and Zhigao, G. (2020). Improving Bayesian network structure learning in the presence of measurement error. arXiv:2011.09776 [cs.AI]
Guo, Z. and Constantinou, A. C. (2020). Approximate learning of high dimensional Bayesian network structures via pruning of Candidate Parent Sets. Entropy, Vol. 22, Iss. 10, Article 1142. [Open-access DOI]
Chobtham, K. and Constantinou, A. C. (2020). Bayesian network structure learning with causal effects in the presence of latent variables. In Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM-2020), Aalborg, Denmark. [arXiv:2005.14381]
Constantinou, A. C. (2020). Learning Bayesian Networks that enable full propagation of evidence. IEEE Access, Vol. 8, pp. 124845-124856 [Open-Access DOI].
Fenton, N., Neil, M., & Constantinou, A. (2020). The Book of Why: The New Science of Cause and Effect, Judea Pearl, Dana Mackenzie, Basic Books (2018). Artificial Intelligence, Vol. 284, Article 103286. [DOI]
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