Results 11 to 20 of about 1,244,068 (326)
, 2016 Structured high-cardinality data arises in many domains, and poses a major challenge for both modeling and inference. Graphical models are a popular approach to modeling structured data but they are unsuitable for high-cardinality variables.
Bui, Hung +5 more
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A Tighter Bound for Graphical Models [PDF]
Neural Computation, 2001 We present a method to bound the partition function of a Boltzmann machine neural network with any odd-order polynomial. This is a direct extension of the mean-field bound, which is first order. We show that the third-order bound is strictly better than mean field.
M.A.R. Leisink, Hilbert J. Kappen
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Statistical Science, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Michael I. Jordan
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Functional Graphical Models [PDF]
Journal of the American Statistical Association, 2018 Graphical models have attracted increasing attention in recent years, especially in settings involving high-dimensional data. In particular, Gaussian graphical models are used to model the conditional dependence structure among multiple Gaussian random variables.
Qiao, Xinghao +2 more
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Elliptical graphical modelling [PDF]
Biometrika, 2011 We propose elliptical graphical models based on conditional uncorrelatedness as a general- ization of Gaussian graphical models by letting the population distribution be elliptical instead of normal, allowing the fitting of data with arbitrarily heavy tails.
D. Vogel, R. Fried
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Graphical Models for Extremes [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2020 SummaryConditional independence, graphical models and sparsity are key notions for parsimonious statistical models and for understanding the structural relationships in the data. The theory of multivariate and spatial extremes describes the risk of rare events through asymptotically justified limit models such as max-stable and multivariate Pareto ...
Sebastian Engelke, Adrien S. Hitz
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Transforming Graphical System Models to Graphical Attack Models [PDF]
, 2016 Manually identifying possible attacks on an organisation is a complex undertaking; many different factors must be considered, and the resulting attack scenarios can be complex and hard to maintain as the organisation changes. System models provide a systematic representation of organisations that helps in structuring attack identification and can ...
Ivanova, Marieta Georgieva +3 more
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Probabilistic Graphical Models [PDF]
, 2014 This report presents probabilistic graphical models that are based on imprecise probabilities using a comprehensive language. In particular, the discussion is focused on credal networks and discrete domains. It describes the building blocks of credal networks, algorithms to perform inference, and discusses on complexity results and related work.
Antonucci, Alessandro +2 more
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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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