The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
A Tutorial on Learning with Bayesian Networks
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
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Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
A practical Bayesian framework for backpropagation networks
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.448 · Cited by 2,917
Robins, J. (1986) · Mathematical Modelling · doi:10.1016/0270-0255(86)90088-6 · Cited by 2,451
Causal diagrams for empirical research
PEARL, J. (1995) · Biometrika · doi:10.1093/biomet/82.4.669 · Cited by 2,355
Bernardo, J. M., & Smith, A. F. M. (1994) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316870 · Cited by 2,178
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
Dawid, A. P. (1984) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2981683 · Cited by 1,085
Social Class, Parental Encouragement, and Educational Aspirations
Sewell, W. H., & Shah, V. P. (1968) · American Journal of Sociology · doi:10.1086/224530 · Cited by 457
Cited by
Friedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742
An Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999) · Machine Learning · doi:10.1023/a:1007665907178 · Cited by 3,791
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332
Anomaly-based network intrusion detection: Techniques, systems and challenges
García-Teodoro, P., Díaz-Verdejo, J., Maciá-Fernández, G., & Vázquez, E. (2009) · Computers & Security · doi:10.1016/j.cose.2008.08.003 · Cited by 2,085
Learning and Revising User Profiles: The Identification of Interesting Web Sites
Pazzani, M., & Billsus, D. (1997) · Machine Learning · doi:10.1023/a:1007369909943 · Cited by 1,202
Metadata
- Authors
- Heckerman, D.
- Venue
- Learning in Graphical Models
- Publisher
- Springer Netherlands
- Published
- 1998
- Pages
- 301-354
- OpenAlex
- W2397866408
- Cited by
- 1,220
- References
- 10
- FWCI
- 10.11
- Type
- book chapter
- Language
- en
- Open access
- green
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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- CoChat. (2026). Citation check: A Tutorial on Learning with Bayesian Networks. https://app.cochat.ai/papers/10.1007/978-94-011-5014-9_11
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- Heckerman, D. (1998). A Tutorial on Learning with Bayesian Networks. Learning in Graphical Models. https://doi.org/10.1007/978-94-011-5014-9_11
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.
