Results 31 to 40 of about 1,309,109 (281)
Lifted graphical models: a survey [PDF]
Machine Learning, 2014 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kimmig, Angelika +2 more
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DragDL:An Easy-to-Use Graphical DL Model Construction System [PDF]
Jisuanji kexue, 2021 Deep learning has broad applications in various fields.However,users still need to face problems from two aspects when applying deep learning.First,deep learning has a complex theoretical background,non-professional users lack background knowledge in ...
TANG Shi-zheng, ZHANG Yan-feng
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Incomplete graphical model inference via latent tree aggregation [PDF]
, 2018 Graphical network inference is used in many fields such as genomics or ecology to infer the conditional independence structure between variables, from measurements of gene expression or species abundances for instance.
Ambroise, Christophe +2 more
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Probabilistic Community Using Link and Content for Social Networks
IEEE Access, 2017 Community detection is one of the most important problems in social network analysis in the context of the structure of underlying graphs. Many researchers have proposed methods, which only consider the network structure of social networks, for ...
Shuai Zhao, Le Yu, Bo Cheng
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Undirected Structural Markov Property for Bayesian Model Determination
Mathematics, 2023 This paper generalizes the structural Markov properties for undirected decomposable graphs to arbitrary ones. This helps us to exploit the conditional independence properties of joint prior laws to analyze and compare multiple graphical structures, while
Xiong Kang, Yingying Hu, Yi Sun
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AlgorithmsWe consider the estimation of the marginal likelihood in Bayesian statistics, with primary emphasis on Gaussian graphical models, where the intractability of the marginal likelihood in high dimensions is a frequently researched problem.
Eric Chuu +3 more
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Fast and Privacy-Preserving Federated Joint Estimator of Multi-sUGMs
IEEE Access, 2021 Learning multiple related graphs from many distributed and privacy-required resources is an important and common task in neuroscience applications. Medical researchers can comprehensively investigate the diagnostic evidence and understand the cause of ...
Xiao Tan, Tianyi Ma, Tongtong Su
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Statistical Science, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Projective Latent Dependency Forest Models
IEEE Access, 2019 Latent dependence forest models (LDFM) are a new type of probabilistic models with the advantage of not requiring the difficult procedure of structure learning in model learning.
Yong Jiang, Yang Zhou, Kewei Tu
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Equivalence model: A new graphical model for causal inference [PDF]
Epidemiology and Health, 2020 Although several causal models relevant to epidemiology have been proposed, a key question that has remained unanswered is why some people at high-risk for a particular disease do not develop the disease while some people at low-risk do develop it.
Jalal Poorolajal
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