References
- Airoldi, E. M. (2007). Getting started in probabilistic graphical models, PLoS Computational Biology, 3, 2421-2425. https://doi.org/10.1371/journal.pcbi.0030252
- Bielza, C. and Larranaga, P. (2014). Discrete Bayesian network classifiers: a survey, ACM Computing Surveys, 47, 1-43. https://doi.org/10.1145/2576868
- Cheng, J. and Greiner, R. (1999). Comparing Bayesian network classifiers. In Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publishers, 101-108.
- Chow, C. and Liu, C. (1968). Approximating discrete probability distributions with dependence trees, IEEE Transactions On Information Theory, 14, 462-467. https://doi.org/10.1109/TIT.1968.1054142
- Epskamp, S., Waldorp, L. J., Mottus, R., and Borsboom, D. (2018). The Gaussian graphical model in cross-sectional and time-series data, Multivariate Behavioral Research, 53, 453-480. https://doi.org/10.1080/00273171.2018.1454823
- Friedman, N., Geiger, D., and Goldszmid, M. (1997). Bayesian network classifiers, Machine Learning, 29, 131-163. https://doi.org/10.1023/A:1007465528199
- Hand, D. J. and Yu, K. (2001). Idiot's Bayes - not so stupid after all?, International Statistical Review, 69, 385-398. https://doi.org/10.1111/j.1751-5823.2001.tb00465.x
- Hansen, K. D., Gentry, J., Long, L., Gentleman, R., Falcon, S., Hahne, F., and Sarkar, D. (2017). Rgraphviz: Provides plotting capabilities for R graph objects. URL https://doi.org/10.18129/B9.bioc.Rgraphviz. R package version 2.20.0.
- Horny, M. (2014). Bayesian networks (Technical Report), Boston University School of Public Health.
- Keogh, E. J. and Pazzani, M. J. (2002). Learning the structure of augmented Bayesian classifiers, International Journal on Artificial Intelligence Tools, 11, 587-601. https://doi.org/10.1142/S0218213002001052
- Leung, D., Drton, M., and Hara, H. (2016). Identifiability of directed Gaussian graphical models with one latent source, Electronic Journal of Statistics, 10, 394-422. https://doi.org/10.1214/16-EJS1111
- Margaritis, D. (2003). Learning Bayesian network model structure from data. Ph.D thesis, Carnegie Mellon University, School of Computer Science.
- Mihaljevic, B., Bielza, C., and Larranaga, P. (2018). bnclassify: Learning Discrete Bayesian Network Classifiers from Data. URL https://CRAN.R-project.org/package=bnclassify. R package version 0.4.0.
- Park, G. (2019). Discovering a fine dust pathway via directed acyclic graphical models, Journal of the Korean Data & Information Science Society, 30, 67-76. https://doi.org/10.7465/jkdi.2019.30.1.67
- Rish, I. (2001). An empirical study of the naive Bayes classifier, IJCAI 2001 workshop on empirical methods in artificial intelligence, 3, 41-46.
-
Robins, G., Pattison, P., Kalish, Y., and Lusher, D. (2007). An introduction to exponential random graph (
$p^{\ast}$ ) models for social networks, Social Networks, 29, 173-191. https://doi.org/10.1016/j.socnet.2006.08.002 - Sahami, M. (1996). Learning limited dependence Bayesian classifiers. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 335-338.
- Webb, G. I., Boughton, J., and Wang, Z. (2005). Not so naive Bayes: Aggregating one-dependence estimators, Machine Learning, 58, 5-24. https://doi.org/10.1007/s10994-005-4258-6
- Zheng, F. and Webb, G.I. (2006). Efficient lazy elimination for averaged one-dependence estimators. In Proceedings of the 23rd International Conference on Machine Learning, 148, 1113-1120.