Results 31 to 40 of about 84,563 (261)
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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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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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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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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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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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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Holistic Approach Promotes Failure Prevention of Smart Mining Machines Based on Bayesian Networks
Machines, 2023 In the forthcoming era of fully autonomous mining, spanning from drilling operations to port logistics, novel approaches will be essential to pre-empt hazardous situations in the absence of human intervention. The progression towards complete autonomy in
Madeleine Martinsen +3 more
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Reconstructing enzyme evolution by protein engineering
FEBS Letters, EarlyView.Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler +2 more
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Bayesian optimization on networks
Journal of Computational PhysicsThis paper studies optimization on networks modeled as metric graphs. Motivated by applications where the objective function is expensive to evaluate or only available as a black box, we develop Bayesian optimization algorithms that sequentially update a Gaussian process surrogate model of the objective to guide the acquisition of query points.
W. Li, D. Sanz-Alonso, R. Yang
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