Results 31 to 40 of about 80,384 (252)
Balanced Quantum-Like Bayesian Networks
Entropy, 2020 Empirical findings from cognitive psychology indicate that, in scenarios under high levels of uncertainty, many people tend to make irrational decisions.
Andreas Wichert +2 more
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Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee [PDF]
Pesquisa Agropecuária Brasileira, 2017 : The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica).
Gabi Nunes Silva +9 more
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Loop amplitudes from precision networks
SciPost Physics Core, 2023 Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably.
Simon Badger, Anja Butter, Michel Luchmann, Sebastian Pitz, Tilman Plehn
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Modeling Incomplete Knowledge of Semantic Web Using Bayesian Networks
Applied Artificial Intelligence, 2019 Interoperable ontologies already exist in the biomedical field, enabling scientists to communicate with minimum ambiguity. Unfortunately, ontology languages, in the semantic web, such as OWL and RDF(S), are based on crisp logic and thus they cannot ...
Messaouda Fareh
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Journal of Industrial Engineering and Management, 2016 Purpose: Propose a modeling and analysis methodology based on the combination of Bayesian networks and Petri networks of the reverse logistics integrated the direct supply chain.
Faycal Mimouni, Abdellah Abouabdellah
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Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers
Complexity, 2018 Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization.
Gonzalo A. Ruz, Pamela Araya-Díaz
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Bayesian networks in neuroscience: A survey
Frontiers in Computational Neuroscience, 2014 Bayesian networks are a type of probabilistic graphical modelslie at the intersection between statistics and machine learning.They have been shown to be powerful tools to encode dependence relationshipsamong the variables of a domain under uncertainty ...
Concha eBielza, Pedro eLarrañaga
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Machine Learning based on Probabilistic Models Applied to Medical Data: The Case of Prostate Cancer
Journal of Innovation Information Technology and Application, 2023 The growth in the amount of data in companies puts analysts in difficulties when extracting hidden knowledge from data. Several models have emerged that focus on the notion of distances while ignoring the notion of conditional probability density.
Anaclet Tshikutu Bikengela +4 more
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Bayesian networks approach on intelligent system design for the diagnosis of heat exchanger
Jurnal Ilmiah SINERGI, 2022 The heat exchanger highly influences the series of cooling processes. Therefore, it is required to have maximum performance. Some of the factors causing a decrease in its performance are increased pressure drop in the Plate Heat Exchanger (PHE ...
Dedik Romahadi +4 more
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Solving inference problems of Bayesian networks by probabilistic computing
AIP Advances, 2023 Recently, probabilistic computing approach has shown its broad application in problems ranging from combinatorial optimizations and machine learning to quantum simulation where a randomly fluctuating bit called p-bit constitutes a basic building block ...
Seokmin Hong
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