"Benchmark Datasets for Bayesian Network Learning: ALARM and CHILD"
收藏DataCite Commons2026-01-05 更新2026-05-03 收录
下载链接：
https://ieee-dataport.org/documents/dataan-interleaved-deletion-markov-boundary-discovery-algorithm-robust-causal-feature
下载链接
链接失效反馈官方服务：
资源简介：
"This dataset contains data samples derived from two classic Bayesian Network benchmarks: ALARM and CHILD. The ALARM network was originally designed for monitoring patients in intensive care (Beinlich et al., 1989), consisting of 37 nodes and 46 arcs. The CHILD network is a probabilistic expert system for diagnosing congenital heart disease (Spiegelhalter and Cowell, 1992), containing 20 nodes and 25 arcs. These datasets are widely used for evaluating Bayesian structure learning and parameter estimation algorithms. The versions provided here are formatted for algorithm performance evaluation and were sourced based on the repositories from bnlearn.References: I. A. Beinlich, H. J. Suermondt, R. M. Chavez, and G. F. Cooper. The ALARM Monitoring System: A Case Study with Two Probabilistic Inference Techniques for Belief Networks. In Proceedings of the 2nd European Conference on Artificial Intelligence in Medicine, pages 247-256. Springer-Verlag, 1989.D. J. Spiegelhalter, R. G. Cowell (1992). Learning in probabilistic expert systems. In Bayesian Statistics 4 (J. M. Bernardo, J. 0. Berger, A. P. Dawid and A. F. M. Smith, eds.), 447-466. Clarendon Press, Oxford."
提供机构：
IEEE DataPort
创建时间：
2026-01-05
