Results 1 to 10 of about 749,160 (179)
Stratified Graphical Models - Context-Specific Independence in Graphical Models
Bayesian Analysis, 2014 19 pages, 7 png figures. In version two the women and mathematics example is replaced with a parliament election data example.
Nyman, Henrik +3 more
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Statistical Science, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Michael I Jordan
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Graphical Markov Models: Overview [PDF]
, 2015 We describe how graphical Markov models started to emerge in the last 40 years, based on three essential concepts that had been developed independently more than a century ago. Sequences of joint or single regressions and their regression graphs are singled out as being best suited for analyzing longitudinal data and for tracing developmental pathways.
Wermuth, Nanny, Cox, D. R.
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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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Heterogeneous Reciprocal Graphical Models [PDF]
Biometrics, 2017 Summary We develop novel hierarchical reciprocal graphical models to infer gene networks from heterogeneous data. In the case of data that can be naturally divided into known groups, we propose to connect graphs by introducing a hierarchical prior across group-specific graphs, including a correlation on edge strengths across graphs ...
Yang Ni +3 more
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Sum–product graphical models [PDF]
Machine Learning, 2019 This paper introduces a new probabilistic architecture called Sum-Product Graphical Model (SPGM). SPGMs combine traits from Sum-Product Networks (SPNs) and Graphical Models (GMs): Like SPNs, SPGMs always enable tractable inference using a class of models that incorporate context specific independence.
Mattia Desana, Christoph Schnörr
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Positivity for Gaussian graphical models [PDF]
, 2012 Gaussian graphical models are parametric statistical models for jointly normal random variables whose dependence structure is determined by a graph. In previous work, we introduced trek separation, which gives a necessary and sufficient condition in ...
Draisma, Jan +2 more
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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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Quantum Graphical Models and Belief Propagation [PDF]
, 2007 Belief Propagation algorithms acting on Graphical Models of classical probability distributions, such as Markov Networks, Factor Graphs and Bayesian Networks, are amongst the most powerful known methods for deriving probabilistic inferences amongst large
Accardi +39 more
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Graphical Models for Optimal Power Flow [PDF]
, 2016 Optimal power flow (OPF) is the central optimization problem in electric power grids. Although solved routinely in the course of power grid operations, it is known to be strongly NP-hard in general, and weakly NP-hard over tree networks.
Chertkov, Michael +4 more
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