The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
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“Heckerman, Geiger and Chickering (1994) provided corresponding closed-form expressions for complete-data likelihoods and posterior model probabilities.”
From Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors (1999) · cites this paper as Heckerman, Geiger and Chickering (1994)
Citations
Cites
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
Research works printed but not linked: 12
- Beinlich, I., Suermondt, H., Chavez, R., and Cooper, G. (1989). In Proceedings of the Second European Conference on Artificial Intelligence in Medicine, London. Springer Verlag, Berlin.
- Cooper, 1992, Machine Learning, 9, 309
- Dawid, 1993, Annals of Statistics, 21, 1272, 10.1214/aos/1176349260 · DOI
- Geiger, D. and Heckerman, D. (1994). In this proceedings.
- Heckerman, D. and Shachter, R. (1994). In this proceedings.
- Lam, W. and Bacchus, F. (1993). In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 243–250. Morgan Kaufmann.
- Madigan, D. and Rafferty, A. E. (1994). Journal of the American Statistical Association, to appear.
- Pearl, J. and Verma, T. (1991). In Knowledge Representation and Reasoning: Proceedings of the Second International Conference, pages 441–452. Morgan Kaufmann, New York.
- Spiegelhalter, 1993, Statistical Science, 8, 219, 10.1214/ss/1177010888 · DOI
- Spirtes, P., Glymour, C., and Scheines, R. (1993). Springer-Verlag, New York.
- Winkler, 1967, American Statistical Association Journal, 62, 776, 10.1080/01621459.1967.10500894 · DOI
- York, J. (1992). PhD thesis, Department of Statistics, University of Washington, Seattle.
Cited by
Sorted by citations
Friedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742
Self-citationHoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999) · Statistical Science · doi:10.1214/ss/1009212519 · Cited by 4,118
Obtaining well calibrated probabilities using Bayesian binning
Pakdaman Naeini, M., Cooper, G., & Hauskrecht, M. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9602 · Cited by 991
An introduction and survey of estimation of distribution algorithms
Hauschild, M., & Pelikan, M. (2011) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2011.08.003 · Cited by 478
Chaibub Neto, E., Keller, M. P., Attie, A. D., & Yandell, B. S. (2010) · The Annals of Applied Statistics · doi:10.1214/09-aoas288 · Cited by 110
Metadata
- Authors
- Heckerman, D., Geiger, D., & Chickering, D. M.
- Affiliations
- Microsoft (United States) (US)
- Corresponding author
- Heckerman, David
- Geiger, Dan
- Chickering, David M.
- Venue
- Uncertainty Proceedings 1994
- Publisher
- Elsevier
- Published
- 1994
- Pages
- 293-301
- OpenAlex
- W2894257070
- Cited by
- 261
- References
- 1
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 7 · 2023: 7 · 2022: 4 · 2021: 15 · 2020: 8 · 2019: 5 · 2018: 2 · 2017: 9 · 2016: 9 · 2015: 9 · 2014: 8 · 2013: 7 · 2012: 5
- FWCI
- 9.95
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Elsevier eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Keywords
- Bayesian network learning, scoring metrics, prior knowledge, statistical data, knowledge-based learning
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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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