Likelihood-based causal structure learning method
A learning method and likelihood technology, applied in the field of likelihood-based causal structure learning, which can solve problems such as multiple time, cost, and multiple independence tests
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[0041] The specific implementation of the present invention will be further described below:
[0042] A likelihood-based causal structure learning method, including the following steps:
[0043] S1), the default causal network structure diagram G = (X, D), where X = (x 1 ,x 2 ,...,x n ), x i Represents the i-th node, D={x i →x j } Means x i With x j The directed edge, if x j X i The parent node of, then expressed as x i →x j ;
[0044] S2), and define the observation data set O = (o 1 ,o 2 ,...,o n ), where o i =(a 1i ,a 2i ,....a mi ) Represents the i-th node x i Observation data set, a j,i Represents the i-th node x i The jth observation data of;
[0045] S3). Initialize the structure diagram D, and use the observation data of the observation data set O to calculate the initial score S of the structure diagram after initialization. 0 ;
[0046] S4) Then traverse all the nodes in the structure graph after initialization processing in pairs, and compare any two nodes x i With x j Carr...
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