The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE
Abstract
A new approach for learning Bayesian belief networks from raw data is presented. The approach is based on Rissanen's minimal description length (MDL) principle, which is particularly well suited for this task. Our approach does not require any prior assumptions about the distribution being learned. In particular, our method can learn unrestricted multiply‐connected belief networks. Furthermore, unlike other approaches our method allows us to trade off accuracy and complexity in the learned model. This is important since if the learned model is very complex (highly connected) it can be conceptually and computationally intractable. In such a case it would be preferable to use a simpler model even if it is less accurate. The MDL principle offers a reasoned method for making this trade‐off. We also show that our method generalizes previous approaches based on Kullback cross‐entropy. Experiments have been conducted to demonstrate the feasibility of the approach.
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Cites
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Cited by
Friedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742
Learning Bayesian Networks with the bnlearnR Package
Scutari, M. (2010) · Journal of Statistical Software · doi:10.18637/jss.v035.i03 · Cited by 1,733
Bayesian Artificial Intelligence
Korb, K. B., & Nicholson, A. E. (2003) · Chapman & Hall/CRC eBooks · doi:10.1201/9780203491294 · Cited by 1,014
Learning Probabilistic Relational Models
Getoor, L., Friedman, N., Koller, D., & Pfeffer, A. (2001) · Relational Data Mining · doi:10.1007/978-3-662-04599-2_13 · Cited by 933
Learning Bayesian networks from data: An information-theory based approach
Cheng, J., Greiner, R., Kelly, J., Bell, D., & Liu, W. (2002) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00191-1 · Cited by 803
Metadata
- Authors
- Lam, W., & Bacchus, F.
- Venue
- Computational Intelligence
- Publisher
- Wiley
- Published
- 1994-08
- Volume
- 10
- Issue
- 3
- Pages
- 269-293
- ISSN
- 0824-7935 (ISSN-L)
- 1467-8640
- OpenAlex
- W1988814833
- Cited by
- 758
- References
- 25
- FWCI
- 11.20
- Type
- journal article
- Language
- en
- Open access
- closed
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- Lam, W., & Bacchus, F. (1994). LEARNING BAYESIAN BELIEF NETWORKS: AN APPROACH BASED ON THE MDL PRINCIPLE. Computational Intelligence. https://doi.org/10.1111/j.1467-8640.1994.tb00166.x
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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.
