| Citation: | MENG G L，CONG Z L，SONG B，et al. A review of Bayesian network structure learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2025，51（9）：2829-2849 （in Chinese） doi: 10.13700/j.bh.1001-5965.2023.0445
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Bayesian networks, as a tool combining probability theory and graph theory, have the ability to efficiently handle uncertain reasoning and data analysis, and are widely used in various fields to solve complex engineering problems. Furthermore, the model can be learned by combining prior knowledge and training samples, overcoming the limitations of establishing the model solely relying on expert knowledge. Based on this, the development history of Bayesian networks was reviewed. The proposed Bayesian network structure learning algorithms were classified and summarized from three aspects: constraint-based methods, rating-based methods, and hybrid search algorithms respectively, and the current research status of various algorithms was summarized and analyzed. Since the data in practical applications often have incompleteness, the research status of incomplete Bayesian network structure learning is explained from two dimensions: missing data processing and latent variable learning. The application of Bayesian networks in different fields is expounded and summarized, and the development trend of future research on Bayesian network structure learning algorithms is discussed.
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