Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas
By Philippe Weber, Gabriela Medina-Oliva, Christophe Simon, Benoît Iung. Engineering applications of artificial intelligence, 2012
768 citations recorded by the source corpus.
Subfields: Safety, Risk, Reliability and Quality, Software, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/16b289b6b954c1440b090be3bbe41b6ab75ad1a3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Safety, Risk, Reliability and Quality
- Andreas Krause
- Faisal I. Khan
- Bernhard Schölkopf
- Nima Khakzad
- Paul Amyotte
- Enrico Zio
- Yiannis Papadopoulos
- Sohag Kabir
In Software
- Andreas Krause
- Faisal I. Khan
- Nima Khakzad
- Paul Amyotte
- Miltiadis Allamanis
- Michael Pradel
- Premkumar T. Devanbu
In Statistics and Probability
- Jessica Taylor
- Andreas Krause
- Faisal I. Khan
- Bernhard Schölkopf
- Patrick LaVictoire
- Eliezer Yudkowsky
- Nima Khakzad
- Andrew Critch
Citations in the corpus
- Alignment for Advanced Machine Learning Systems (2020, background): “Bayesian networks (Friedman, Geiger, and Goldszmidt 1997; Pearl 2009) have been applied in many domains, including ones where reliability and interpretability are concerns (Weber et al. 2012).”
- Safety analysis in process facilities: Comparison of fault tree and Bayesian network approaches (2011, methodology): “[30] gave an exhaustive statistical review of BN application in different areas such as risk and maintenance analysis, in which BN was qualitatively compared with other methods such as FTs, Markov chains, and Petri nets.”
- DiBS: Differentiable Bayesian Structure Learning (2021, background): “Bayesian networks (BNs) [1] and structural equation models are commonly used for this purpose [2–5].”