计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
19期
192-198
,共7页
贝叶斯网络%断面调查数据%因果模型%结构学习%因果关系%医学论证
貝葉斯網絡%斷麵調查數據%因果模型%結構學習%因果關繫%醫學論證
패협사망락%단면조사수거%인과모형%결구학습%인과관계%의학론증
Bayesian network%cross-sectional survey data%causal model%structure learning%causal relations%medical arguments
研究目的是验证连续贝叶斯网络模型可以从断面调查数据获取因果信息。使用L1MB、TC、PCB和Two-Phase等连续贝叶斯网络结构学习算法,从美国健康和营养调查(NHANES)提供的真实断面调查数据,获取潜在的因果关系。实验结果表明这些算法能不同程度地从横断面调查数据发现相应的因果关系,适用于高斯和非高斯数据的PCB算法,以及Two-Phase算法的学习性能优于仅适用于高斯数据的L1MB算法和TC算法。结合PCB算法和Two-Phase算法进行因果分析,这样得到的因果结构才较为全面。
研究目的是驗證連續貝葉斯網絡模型可以從斷麵調查數據穫取因果信息。使用L1MB、TC、PCB和Two-Phase等連續貝葉斯網絡結構學習算法,從美國健康和營養調查(NHANES)提供的真實斷麵調查數據,穫取潛在的因果關繫。實驗結果錶明這些算法能不同程度地從橫斷麵調查數據髮現相應的因果關繫,適用于高斯和非高斯數據的PCB算法,以及Two-Phase算法的學習性能優于僅適用于高斯數據的L1MB算法和TC算法。結閤PCB算法和Two-Phase算法進行因果分析,這樣得到的因果結構纔較為全麵。
연구목적시험증련속패협사망락모형가이종단면조사수거획취인과신식。사용L1MB、TC、PCB화Two-Phase등련속패협사망락결구학습산법,종미국건강화영양조사(NHANES)제공적진실단면조사수거,획취잠재적인과관계。실험결과표명저사산법능불동정도지종횡단면조사수거발현상응적인과관계,괄용우고사화비고사수거적PCB산법,이급Two-Phase산법적학습성능우우부괄용우고사수거적L1MB산법화TC산법。결합PCB산법화Two-Phase산법진행인과분석,저양득도적인과결구재교위전면。
This study is to verify that the continuous Bayesian Network model can be used to discover causal information from cross-sectional survey data. Using the L1MB algorithm, TC, PCB and Two-Phase algorithm, this paper analyzes causal relations in the real data from the National Health and Nutrition Examination Survey. Experimental results show that these algorithms can discover causal relations to various degrees. The PCB algorithm and Two-Phase algorithm that apply to Gaussian or non-Gaussian data outperform the L1MB algorithm and TC algorithm that only apply to Gaussian data. Combining the PCB algorithm and Two-Phase algorithm for causal analysis, the causal structure thus obtained is more comprehensive.