计算机应用与软件
計算機應用與軟件
계산궤응용여연건
Computer Applications and Software
2015年
9期
231-234
,共4页
非线性因果模型%因果辨识%非线性成对独立性测试
非線性因果模型%因果辨識%非線性成對獨立性測試
비선성인과모형%인과변식%비선성성대독립성측시
Nonlinear causal models%Causal identification%Nonlinear pairwise independence tests
近来,基于观测变量的因果模型辨识受到了较多关注。一般使用线性无环因果模型对数据生成过程建模,而实际上,许多因果模型包含非线性关系,使用纯线性方法求解是无效的。将线性模型泛化为非线性模型,提出一种两步骤的辨识算法,首先使用特征选择算法获得d分离等价类,然后使用非线性成对独立性测试为图中的边标注因果方向。实验结果验证了该算法的有效性,并表明其优于其他算法。
近來,基于觀測變量的因果模型辨識受到瞭較多關註。一般使用線性無環因果模型對數據生成過程建模,而實際上,許多因果模型包含非線性關繫,使用純線性方法求解是無效的。將線性模型汎化為非線性模型,提齣一種兩步驟的辨識算法,首先使用特徵選擇算法穫得d分離等價類,然後使用非線性成對獨立性測試為圖中的邊標註因果方嚮。實驗結果驗證瞭該算法的有效性,併錶明其優于其他算法。
근래,기우관측변량적인과모형변식수도료교다관주。일반사용선성무배인과모형대수거생성과정건모,이실제상,허다인과모형포함비선성관계,사용순선성방법구해시무효적。장선성모형범화위비선성모형,제출일충량보취적변식산법,수선사용특정선택산법획득d분리등개류,연후사용비선성성대독립성측시위도중적변표주인과방향。실험결과험증료해산법적유효성,병표명기우우기타산법。
The identification of causal models based on observed variables has received much attention in the past.Linear acyclic causal models are usually used to model the data-generating process,but practically many causal relationships are more or less nonlinear,this raises the doubts to the usefulness of purely linear methods.In this paper,we generalise the basic linear model to nonlinear model,and propose a two-step identification method,which first uses feature-selection algorithm to obtain the d-separation equivalence class,and then uses nonlinear pairwise independence tests to mark the causal directions for edges in the image.Experimental results verify the validity of this algorithm and show that it outperforms other methods.