In this paper, a hybrid model is proposed to simultaneously mine causal connections and features responsible for contemporaneous correlations in a multivariate process. The model is developed by combining the vector auto-regressive exogenous model and the factor analysis model. The parameters of the resulting model are regularized using the hierarchical prior distributions for pruning insignificant/irrelevant ones from the model. It is then estimated under the variational Bayesian expectation maximization framework. The estimation is initiated with a complex model which is then systematically reduced to a simpler model that retains only the parameters corresponding to significant causal connections and contemporaneous correlations. Model reduction is carried out through a series of deterministic jumps from complex models to simpler models using a relevance criterion. The approach is illustrated with a number of simulated examples and an industrial case study.

中文翻译：

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工业过程数据中因果关系和同期相关特征推断的变分贝叶斯方法


在本文中，提出了一种混合模型，用于同时挖掘多元过程中的因果联系和负责同期相关性的特征。该模型是结合向量自回归外生模型和因子分析模型建立的。使用分层先验分布对所得模型的参数进行正则化，以从模型中修剪不重要/不相关的参数。然后在变分贝叶斯期望最大化框架下进行估计。估计是从一个复杂的模型开始的，然后系统地简化为一个更简单的模型，该模型仅保留与重要因果关系和同期相关性相对应的参数。模型简化是通过使用相关性标准从复杂模型到更简单模型的一系列确定性跳跃来实现的。该方法通过许多模拟示例和工业案例研究进行了说明。