Critique of: “A Parallel Framework for Constraint-Based Bayesian Network Learning via Markov Blanket Discovery” by SCC Team From UC San Diego

Arunav Gupta, John Ge,John Li,Zihao Kong, Kaiwen He, Matthew Mikhailov, Bryan Chin,Xiaochen Li,Max Apodaca,Paul Rodriguez,Mahidar Tatineni,Mary Thomas, Santosh Bhatt

IEEE Transactions on Parallel and Distributed Systems(2023)

引用 1|浏览3
暂无评分
摘要
Bayesian networks (BNs) have become popular in recent years to describe natural phenomena in situations where causal linkages are important to understand. In order to get around the inherent non-tractability of learning BNs, Srivastava et al. propose a markov blanket discovery-based approach to learning in their paper titled “A Parallel Framework for Constraint-based Bayesian Network Learning via Markov Blanket Discovery.” We are able to reproduce both the strong and weak scaling experiments from the paper up to 128 cores, and verify communication cost scaling for all three algorithms in the paper. We also introduce methodological improvements to weak scaling that show the paper's findings are unique to the methodology and not the datasets used. Slight variations in performance were observed due to differences in datasets, core count, and job scheduling.
更多
查看译文
关键词
bayesian network learning,markov blanket discovery”,constraint-based
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要