Results 41 to 50 of about 3,103,450 (291)
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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Monotonicity in Bayesian Networks
CoRR, 2004 Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004)
van der Gaag, L.C. +2 more
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Bayesian networks to explain the effect of label information on product perception [PDF]
, 2011 Interdisciplinary approaches in food research require new methods in data analysis that are able to deal with complexity and facilitate the communication among model users. Four parallel full factorial within-subject designs were performed to examine the
Kole, A.P.W. +4 more
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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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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 dynamic reliability using dynamic Bayesian networks [PDF]
, 2006 This paper considers the problem of modeling and analyzing the reliability of a system or a component (system) where the state of the system and the state of process variables influences each other in addition to an exogenous perturbation influence: this
Noyes, Daniel, Tchangani, Ayeley
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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 generalized network design [PDF]
Theoretical Computer Science, 2020 25 pages, 0 figure. An extended abstract of this paper is to appear in the 27th Annual European Symposium on Algorithms (ESA 2019)
Yuval Emek +3 more
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GENERATIONS IN BAYESIAN NETWORKS
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 2019 This paper focuses on the study of some aspects of the theory of oriented graphs in Bayesian networks. In some papers on the theory of Bayesian networks, the concept of “Generation of vertices” denotes a certain set of vertices with many parents ...
Alexander Litvinenko +3 more
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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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