Results 31 to 40 of about 133,961 (310)
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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Widening Access to Bayesian Problem Solving
Frontiers in Psychology, 2020 Bayesian reasoning and decision making is widely considered normative because it minimizes prediction error in a coherent way. However, it is often difficult to apply Bayesian principles to complex real world problems, which typically have many unknowns ...
Nicole Cruz +8 more
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Monotonicity in Bayesian Networks
, 2004 Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004)
Hans L. Bodlaender +2 more
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Opinion Dynamics with Bayesian Learning
Complexity, 2020 Bayesian learning is a rational and effective strategy in the opinion dynamic process. In this paper, we theoretically prove that individual Bayesian learning can realize asymptotic learning and we test it by simulations on the Zachary network.
Aili Fang +3 more
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A Bayesian network structure learning method for optimizing ordering search operator
Xibei Gongye Daxue Xuebao, 2023 Local search algorithm in ordering space is a good method which can effectively improve the efficiency of bayesian network structure learning. However, the existing algorithms usually have problems such as insufficient order optimization, low learning ...
JIA Liuna +4 more
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Quantification of an Adverse Outcome Pathway Network by Bayesian Regression and Bayesian Network Modeling [PDF]
Integrated Environmental Assessment and Management, 2020 Abstract The adverse outcome pathway (AOP) framework has gained international recognition as a systematic approach linking mechanistic processes to toxicity endpoints. Nevertheless, successful implementation into risk assessments is still limited by the lack of quantitative AOP models (qAOPs) and assessment of uncertainties.
Raoul Wolf +7 more
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Inversion of Bayesian networks
International Journal of Approximate ReasoningVariational autoencoders and Helmholtz machines use a recognition network (encoder) to approximate the posterior distribution of a generative model (decoder). In this paper we study the necessary and sufficient properties of a recognition network so that it can model the true posterior distribution exactly.
Jesse van Oostrum +2 more
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Digital Health, 2022 Background Community-acquired pneumonia is one of the most common infectious diseases in children and is a leading cause of death among children under 5 years of age, resulting in high rates of antibiotic usage and hospitalization.
Jing Li +10 more
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Network Formation with Asymmetric Players and Chance Moves
Mathematics, 2021 We propose a model of network formation as a two-stage game with chance moves and players of various types. First, the leader suggests a connected communication network for the players to join.
Ping Sun, Elena Parilina
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Arthritis Care &Research, EarlyView.Objective Fibromyalgia is a chronic condition characterized by widespread musculoskeletal pain and fatigue. Almost everyone with fibromyalgia has sleep problems. We aimed to evaluate the effectiveness and safety of current interventions for the management of fibromyalgia‐related sleep problems.
Jemma Hudson +11 more
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