The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Bayesian Networks
Open accessAbstract
Bayesian networks are probabilistic models based on direct acyclic graphs. These models enable a direct representation of causal relations between variables. Their structure is ideal for combining prior knowledge, which often comes in causal form, and observed data. This article gives a short description of the concepts of this important class of models that have become extremely popular in recent years.
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Cites
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Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
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Friedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742
Causation, Prediction, and Search
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An Introduction to Variational Methods for Graphical Models
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A Tutorial on Learning with Bayesian Networks
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Theory-based Bayesian models of inductive learning and reasoning
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C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems
Alam, K. M., & El Saddik, A. (2017) · IEEE Access · doi:10.1109/access.2017.2657006 · Cited by 741
Pessach, D., Singer, G., Avrahami, D., Chalutz Ben-Gal, H., Shmueli, E., & Ben-Gal, I. (2020) · Decision Support Systems · doi:10.1016/j.dss.2020.113290 · Cited by 304
Self-citationAlShorman, O., Irfan, M., Saad, N., Zhen, D., Haider, N., Glowacz, A., & AlShorman, A. (2020) · Shock and Vibration · doi:10.1155/2020/8843759 · Cited by 215
Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges
Wang, S., Qureshi, M. A., Miralles-Pechuán, L., Huynh-The, T., Gadekallu, T. R., & Liyanage, M. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3386872 · Cited by 170
Metadata
- Authors
- Ben‐Gal, I.
- Venue
- Encyclopedia of Statistics in Quality and Reliability
- Publisher
- Wiley
- Published
- 2007-12-14
- OpenAlex
- W3191596906
- Cited by
- 325
- References
- 18
- Type
- reference entry
- Language
- en
- Open access
- bronze
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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.
