2018/06/03 by Mohammad Ali Javidian, Javidian, Mohammad Ali, Marco Valtorta +1
Computer Science · Decision Sciences · #Artificial Intelligence (cs.AI) #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Multi-Criteria Decision Making #cs.AI
paper · pdf · doi:10.48550/arxiv.1806.00882
19 pages, 6 figures
openalex publication_date 2018/06/03 · arxiv created 2020/02/24 · arxiv updated 2020/02/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We extend the decomposition approach for learning Bayesian networks (BNs) proposed by (Xie et. al.) to learning multivariate regression chain graphs (MVR CGs), which include BNs as a special case. The same advantages of this decomposition approach hold in the more general setting: reduced complexity and increased power of computational independence tests. Moreover, latent (hidden) variables can be represented in MVR CGs by using bidirected edges, and our algorithm correctly recovers any independence structure that is faithful to an MVR CG, thus greatly extending the range of applications of decomposition-based model selection techniques. Simulations under a variety of settings demonstrate the competitive performance of our method in comparison with the PC-like algorithm (Sonntag and Pena). In fact, the decomposition-based algorithm usually outperforms the PC-like algorithm except in running time. The performance of both algorithms is much better when the underlying graph is sparse.