Informed machine learning–a taxonomy and survey of integrating prior knowledge into learning systems
Despite its great success, machine learning can have its limits when dealing with insufficient
training data. A potential solution is the additional integration of prior knowledge into the …
training data. A potential solution is the additional integration of prior knowledge into the …
A survey of Bayesian Network structure learning
Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …
Evolution of safety and security risk assessment methodologies towards the use of bayesian networks in process industries
PG George, VR Renjith - Process Safety and Environmental Protection, 2021 - Elsevier
Process Industries handling, producing and storing bulk amount of hazardous materials are
a major source of concern in terms of both safety and security. Safety and security cannot be …
a major source of concern in terms of both safety and security. Safety and security cannot be …
Learning Bayesian networks: approaches and issues
Bayesian networks have become a widely used method in the modelling of uncertain
knowledge. Owing to the difficulty domain experts have in specifying them, techniques that …
knowledge. Owing to the difficulty domain experts have in specifying them, techniques that …
Software project risk analysis using Bayesian networks with causality constraints
Many risks are involved in software development and risk management has become one of
the key activities in software development. Bayesian networks (BNs) have been explored as …
the key activities in software development. Bayesian networks (BNs) have been explored as …
Causal discovery with language models as imperfect experts
Understanding the causal relationships that underlie a system is a fundamental prerequisite
to accurate decision-making. In this work, we explore how expert knowledge can be used to …
to accurate decision-making. In this work, we explore how expert knowledge can be used to …
Convolutional neural network-based Bayesian Gaussian mixture for intelligent fault diagnosis of rotating machinery
Fault diagnosis is very important to ensure the efficiency and reliability of rotating machinery.
Traditional fault diagnosis methods often require manual feature design and extraction …
Traditional fault diagnosis methods often require manual feature design and extraction …
Exploiting experts' knowledge for structure learning of Bayesian networks
Learning Bayesian network structures from data is known to be hard, mainly because the
number of candidate graphs is super-exponential in the number of variables. Furthermore …
number of candidate graphs is super-exponential in the number of variables. Furthermore …
Biosecurity risk factors for highly pathogenic avian influenza (H5N8) virus infection in duck farms, France
Highly pathogenic avian influenza (HPAI) subtype H5N8 outbreaks occurred in poultry farms
in France in 2016–2017, resulting in significant economic losses and disruption to the …
in France in 2016–2017, resulting in significant economic losses and disruption to the …
A modelling approach based on Bayesian networks for dam risk analysis: Integration of machine learning algorithm and domain knowledge
X Tang, A Chen, J He - International Journal of Disaster Risk Reduction, 2022 - Elsevier
The safety of dams, especially that of earthen dams, is threatened by various uncertain and
interrelated risk factors. Consequently, dam risk analysis is vital for dam safety governance …
interrelated risk factors. Consequently, dam risk analysis is vital for dam safety governance …