Results 11 to 20 of about 1,036,354 (257)
Graphical Models, 2023 Modern laser scanners, depth sensor devices and Dense Image Matching techniques allow for capturing of extensive point cloud datasets. While capturing has become more user-friendly, the size of registered point clouds results in large datasets which pose
Jan Martens, Jörg Blankenbach
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Modeling multi-style portrait relief from a single photograph
Graphical Models, 2023 This paper aims at extending the method of Zhang et al. (2023) to produce not only portrait bas-reliefs from single photographs, but also high-depth reliefs with reasonable depth ordering.
Yu-Wei Zhang +6 more
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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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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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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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Getting started in probabilistic graphical models [PDF]
, 2007 Probabilistic graphical models (PGMs) have become a popular tool for computational analysis of biological data in a variety of domains. But, what exactly are they and how do they work?
Airoldi, Edoardo M
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Marginal and simultaneous predictive classification using stratified graphical models [PDF]
, 2014 An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged ...
Corander, Jukka +3 more
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, 2014 Stable random variables are motivated by the central limit theorem for densities with (potentially) unbounded variance and can be thought of as natural generalizations of the Gaussian distribution to skewed and heavy-tailed phenomenon. In this paper, we introduce stable graphical (SG) models, a class of multivariate stable densities that can also be ...
Misra N, Kuruoglu E E
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