Practicalities of Bayesian network modeling for nuclear data evaluation with the nucdataBaynet package [PDF]
EPJ Web of Conferences, 2023 Bayesian networks are a helpful abstraction in the modelization of the relationships between different variables for the purpose of uncertainty quantification.
Schnabel Georg
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Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems [PDF]
Computer Methods in Applied Mechanics and Engineering, 2022 Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify patterns in big data, and make decision
Kevin Linka +5 more
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Bayesian learning for neural networks: an algorithmic survey [PDF]
Artificial Intelligence Review, 2022 The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of the topic and the multitude of ingredients involved therein, besides the complexity of turning theory into practical implementations, limit the use of the ...
M. Magris, A. Iosifidis
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A tutorial on bayesian networks for psychopathology researchers.
Psychological methods, 2021 Bayesian Networks are probabilistic graphical models that represent conditional independence relationships among variables as a directed acyclic graph (DAG), where edges can be interpreted as causal effects connecting one causal symptom to an effect ...
G. Briganti, M. Scutari, R. McNally
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Using consensus bayesian network to model the reactive oxygen species regulatory pathway. [PDF]
PLoS ONE, 2013 Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data ...
Liangdong Hu, Limin Wang
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Learning Bayesian networks: The combination of knowledge and statistical data [PDF]
Machine-mediated learning, 1994 We describe a Bayesian approach for learning Bayesian networks from a combination of prior knowledge and statistical data. First and foremost, we develop a methodology for assessing informative priors needed for learning.
D. Heckerman +2 more
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Learning oncogenetic networks by reducing to mixed integer linear programming. [PDF]
PLoS ONE, 2013 Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events.
Hossein Shahrabi Farahani +1 more
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Bayesian networks for interpretable machine learning and optimization
Neurocomputing, 2021 As artificial intelligence is being increasingly used for high-stakes applications, it is becoming more and more important that the models used be interpretable. Bayesian networks offer a paradigm for inter-pretable artificial intelligence that is based on
Bojan Mihaljević +2 more
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Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users [PDF]
IEEE Computational Intelligence Magazine, 2020 Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of challenging problems. However, since deep learning methods operate as black boxes, the uncertainty associated with their predictions is often challenging to quantify.
Laurent Valentin Jospin +4 more
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B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data [PDF]
Journal of Computational Physics, 2020 We propose a Bayesian physics-informed neural network (B-PINN) to solve both forward and inverse nonlinear problems described by partial differential equations (PDEs) and noisy data.
Liu Yang, Xuhui Meng, G. Karniadakis
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