Uncertainties in conditional probability tables of discrete Bayesian Belief Networks: A comprehensive review
J Rohmer - Engineering Applications of Artificial Intelligence, 2020 - Elsevier
Abstract Discrete Bayesian Belief Network (BBN) has become a popular method for the
analysis of complex systems in various domains of application. One of its pillar is the …
analysis of complex systems in various domains of application. One of its pillar is the …
[HTML][HTML] Thirty years of credal networks: Specification, algorithms and complexity
Credal networks generalize Bayesian networks to allow for imprecision in probability values.
This paper reviews the main results on credal networks under strong independence, as …
This paper reviews the main results on credal networks under strong independence, as …
Robust data-driven human reliability analysis using credal networks
Despite increasing collection efforts of empirical human reliability data, the available
databases are still insufficient for understanding the relationships between human errors …
databases are still insufficient for understanding the relationships between human errors …
“This Is What We Don't Know”: Treating epistemic uncertainty in Bayesian networks for risk assessment
Failing to communicate current knowledge limitations, that is, epistemic uncertainty, in
environmental risk assessment (ERA) may have severe consequences for decision making …
environmental risk assessment (ERA) may have severe consequences for decision making …
Uncertainty analysis of accident causality model using Credal Network with IDM method: A case study of hazardous material road transportation accidents
S Ding, X Pan, D Zuo, W Zhang, L Sun - Process Safety and Environmental …, 2022 - Elsevier
Bayesian network (BN) is an effective tool for causal inferences of accidents. However, it is
often criticized for the difficulty in obtaining accurate/sufficient data needed to get precise …
often criticized for the difficulty in obtaining accurate/sufficient data needed to get precise …
Bayesian networks with imprecise probabilities: Theory and application to classification
Bayesian networks are powerful probabilistic graphical models for modelling uncertainty.
Among others, classification represents an important application: some of the most used …
Among others, classification represents an important application: some of the most used …
Probabilistic inference in credal networks: new complexity results
DD Mauá, CP de Campos, A Benavoli… - Journal of Artificial …, 2014 - jair.org
Credal networks are graph-based statistical models whose parameters take values in a set,
instead of being sharply specified as in traditional statistical models (eg, Bayesian …
instead of being sharply specified as in traditional statistical models (eg, Bayesian …
Research on human error risk evaluation using extended Bayesian networks with hybrid data
Bayesian networks (BNs) play an important role in performing uncertainty analysis. BNs, as
a sort of directed acyclic graph with probabilities, can establish causality and clarify complex …
a sort of directed acyclic graph with probabilities, can establish causality and clarify complex …
Root cause analysis in lithium-ion battery production with fmea-based large-scale bayesian network
The production of lithium-ion battery cells is characterized by a high degree of complexity
due to numerous cause-effect relationships between process characteristics. Knowledge …
due to numerous cause-effect relationships between process characteristics. Knowledge …
[PDF][PDF] Building knowledge-based systems by credal networks: a tutorial
Abstract Knowledge-based systems are computer programs achieving expert-level
competence in solving problems for specific task areas. This chapter is a tutorial on the …
competence in solving problems for specific task areas. This chapter is a tutorial on the …