Learning Linear Causal Models by MML Sampling

Learning Linear Causal Models by MML Sampling
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通过 MML 采样学习线性因果模型

DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
K. Korb
K. Korb
中科院分区：
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文献类型：
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作者：
C. S. Wallace;K. Korb

文献摘要

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我们将线性因果模型的最小消息长度(MML)估计与蒙特卡罗抽样相结合，生成了一个程序，在给定普通联合样本数据的情况下，报告了因果模型及其成员模型等价类的后验概率。我们将我们的程序与Tourd II[7.15]和Madigan等人的贝叶斯MCMC程序进行了比较。[7.11].我们的方法不同于梅迪根等人的方法。[7.11]尤其是在不将相等的先验概率分配给因果模型的等价类以及在因果联系足够弱以至于不能期望可用的样本数据区分它们时合并来自不同等价类的模型(我们将其称为小效等价)。
We combine Minimum Message Length (MML) evaluation of linear causal models with Monte Carlo sampling to produce a program that, given ordinary joint sample data, reports the posterior probabilities of equivalence classes of causal models and their member models. We compare our program with TETRAD II [7.15] and the Bayesian MCMC program of Madigan et al. [7.11]. Our approach differs from that of Madigan et al. [7.11] particularly in not assigning equal prior probabilities to equivalence classes of causal models and in merging models from distinct equivalence classes when the causal links are sufficiently weak that the sample data available could not be expected to distinguish between them (which we call ‘small effect equivalence’).