Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis

667 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A unified framework for multilevel uncertainty quantification in Bayesian inverse problems (2016, methodology): “A DAG corresponding to this experimental situation is shown in Fig.”
Application of Bayesian Networks in Reliability Evaluation (2019, background): “OOBNs allow for simple model reuse, submodel encapsulation, and model construction in a top–down fashion, a bottom-up fashion, or a mixture of the two [17].”
Bayesian reasoning and machine learning (2012, neutral): “3 Extending Bayesian Networks for Decisions An influence diagram is a Bayesian Network with additional Decision nodes and Utility nodes [137, 148, 162].”
Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches (2011, background): “While BN reduces the uncertainty of prior beliefs through probability updating, there are other modeling techniques that help to capture some types of uncertainty [37].”