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| 베이즈 네트워크× | NOTEARS: 연속 최적화를 통한 인과 구조 학습× | |
|---|---|---|
| 분야≠ | 베이지안 | 인과추론 |
| 계열≠ | Bayesian methods | Machine learning |
| 기원 연도≠ | 1988 | 2018 |
| 창시자≠ | Judea Pearl | Zheng, Aragam, Ravikumar & Xing |
| 유형≠ | Probabilistic graphical model | Continuous optimization algorithm for causal DAG discovery |
| 원전≠ | Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797 | Zheng, X., Aragam, B., Ravikumar, P., & Xing, E. P. (2018). DAGs with NO TEARS: Continuous optimization for structure learning. Advances in Neural Information Processing Systems, 31. link ↗ |
| 별칭≠ | Bayes network, belief network, probabilistic graphical model, directed graphical model | DAGs with NO TEARS, Continuous Structure Learning, Continuous DAG Optimization, Sürekli DAG Yapı Öğrenimi |
| 관련≠ | 4 | 1 |
| 요약≠ | A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others. | NOTEARS (No Tears: Acyclicity Regression Structure) is a causal structure learning algorithm introduced by Zheng, Aragam, Ravikumar, and Xing in 2018 at NeurIPS. It reformulates the combinatorially hard problem of learning a directed acyclic graph (DAG) from observational data as a continuous, smooth optimization problem, enabling the use of standard gradient-based solvers and removing the need for exhaustive combinatorial search over graph space. |
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