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Found 4 papers, 0 papers with code
Interactive and Intelligent Root Cause Analysis in Manufacturing with Causal Bayesian Networks and Knowledge Graphs
In addition, an Interactive User Interface enables a process expert to give feedback to the root cause graph by adding and removing information to the Knowledge Graph.
Boosting Causal Additive Models
We present a boosting-based method to learn additive Structural Equation Models (SEMs) from observational data, with a focus on the theoretical aspects of determining the causal order among variables.
Learning Causal Graphs in Manufacturing Domains using Structural Equation Models
Many production processes are characterized by numerous and complex cause-and-effect relationships.
Estimating Gaussian Copulas with Missing Data
In this work we present a rigorous application of the Expectation Maximization algorithm to determine the marginal distributions and the dependence structure in a Gaussian copula model with missing data.
