The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Learning Bayesian networks from data: An information-theory based approach
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Citations
Cites
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
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Hapeman, D. E. (1991) · Technometrics · doi:10.1080/00401706.1991.10484817 · Cited by 9,373
Friedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742
Causation, Prediction, and Search
Spirtes, P., Glymour, C., & Scheines, R. (1993) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2748-9 · Cited by 4,617
A Bayesian method for the induction of probabilistic networks from data
Cooper, G. F., & Herskovits, E. (1992) · Machine Learning · doi:10.1007/bf00994110 · Cited by 4,204
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
Heckerman, D., Geiger, D., & Chickering, D. M. (1995) · Machine Learning · doi:10.1023/a:1022623210503 · Cited by 3,231
Learning Bayesian networks: The combination of knowledge and statistical data
Heckerman, D., Geiger, D., & Chickering, D. M. (1995) · Machine Learning · doi:10.1007/bf00994016 · Cited by 2,017
Approximating discrete probability distributions with dependence trees
Chow, C., & Liu, C. (1968) · IEEE Transactions on Information Theory · doi:10.1109/tit.1968.1054142 · Cited by 1,720
Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
Madigan, D., & Raftery, A. E. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476894 · Cited by 1,205 · cited 1×
“Therefore, instead of searching for a single best solution, their algorithms [6,36,37] return several networks and use the ‘average’ of these networks to perform belief propagation.”
Background
Cited by
Machine learning: a review of classification and combining techniques
Kotsiantis, S. B., Zaharakis, I. D., & Pintelas, P. E. (2006) · Artificial Intelligence Review · doi:10.1007/s10462-007-9052-3 · Cited by 1,582
Information-Theoretic Inference of Large Transcriptional Regulatory Networks
Meyer, P. E., Kontos, K., Lafitte, F., & Bontempi, G. (2007) · EURASIP Journal on Bioinformatics and Systems Biology · doi:10.1155/2007/79879 · Cited by 273
D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery
Vowels, M. J., Camgoz, N. C., & Bowden, R. (2023) · ACM Computing Surveys · doi:10.1145/3527154 · Cited by 237
Connected and autonomous vehicles: A cyber-risk classification framework
Sheehan, B., Murphy, F., Mullins, M., & Ryan, C. (2019) · Transportation Research Part A: Policy and Practice · doi:10.1016/j.tra.2018.06.033 · Cited by 170
Islam, M. S., Nepal, M. P., Skitmore, M., & Attarzadeh, M. (2017) · Advanced Engineering Informatics · doi:10.1016/j.aei.2017.06.001 · Cited by 163
Metadata
- Authors
- Cheng, J., Greiner, R., Kelly, J., Bell, D., & Liu, W.
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2002-05
- Volume
- 137
- Issue
- 1-2
- Pages
- 43-90
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W1983690667
- Cited by
- 803
- References
- 39
- FWCI
- 22.42
- Type
- journal article
- Language
- en
- Open access
- bronze
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- CoChat. (2026). Citation check: Learning Bayesian networks from data: An information-theory based approach. https://app.cochat.ai/papers/10.1016/s0004-3702(02)00191-1
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- Cheng, J., Greiner, R., Kelly, J., Bell, D., & Liu, W. (2002). Learning Bayesian networks from data: An information-theory based approach. Artificial Intelligence. https://doi.org/10.1016/s0004-3702(02)00191-1
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
