Smooth, identifiable supermodels of discrete DAG models with latent variables
Smooth, identifiable supermodels of discrete DAG models with latent variables
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DOI：
10.3150/17-bej1005
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发表时间：
2015-11
期刊：
影响因子：
1.5
通讯作者：
R. Evans;T. Richardson
中科院分区：
文献类型：
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作者：
R. Evans;T. Richardson
我们提供了一个参数化的离散嵌套马尔可夫模型，这是一个超级模型，近似DAG模型（贝叶斯网络模型）与潜变量。这种模型广泛用于因果推理和机器学习。我们明确评估其尺寸，表明它们是弯曲的指数分布族，并将它们拟合到数据中。参数化避免了潜变量模型的不规则性和不可识别性。使用的参数都是完全可识别和因果解释的数量。
We provide a parameterization of the discrete nested Markov model, which is a supermodel that approximates DAG models (Bayesian network models) with latent variables. Such models are widely used in causal inference and machine learning. We explicitly evaluate their dimension, show that they are curved exponential families of distributions, and fit them to data. The parameterization avoids the irregularities and unidentifiability of latent variable models. The parameters used are all fully identifiable and causally-interpretable quantities.