Results 11 to 20 of about 422,581 (268)
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 ...
Engelke, Sebastian, Hitz, Adrien S.
openaire +3 more sources
On Sufficient Graphical Models
CoRR, 2023 We introduce a sufficient graphical model by applying the recently developed nonlinear sufficient dimension reduction techniques to the evaluation of conditional independence. The graphical model is nonparametric in nature, as it does not make distributional assumptions such as the Gaussian or copula Gaussian assumptions.
Bing Li, Kyongwon Kim
openaire +3 more sources
, 2023 Probabilistic Graphical Models are often used to understand dynamics of a system. They can model relationships between features (nodes) and the underlying distribution. Theoretically these models can represent very complex dependency functions, but in practice often simplifying assumptions are made due to computational limitations associated with graph
SHRIVASTAVA HARSH +1 more
openaire +4 more sources
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 ...
Marieta Georgieva Ivanova +3 more
openaire +2 more sources
J. Mach. Learn. Res., 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
openaire +5 more sources
Graphical Local Genetic Algorithm for High-Dimensional Log-Linear Models
Mathematics, 2023 Graphical log-linear models are effective for representing complex structures that emerge from high-dimensional data. It is challenging to fit an appropriate model in the high-dimensional setting and many existing methods rely on a convenient class of ...
Lyndsay Roach, Xin Gao
doaj +1 more source
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
doaj +1 more source
Annals of Medicine, 2023 Background Probabilistic graphical modelling (PGM) can be used to predict risk at the individual patient level and show multiple outcomes and exposures in a single model.Objective To develop PGM for the prediction of clinical outcome in patients with ...
Dong Ah Shin +6 more
doaj +1 more source
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
openaire +2 more sources
On graphical models and convex geometry
Computational Statistics & Data Analysis, 2023 We introduce a mixture-model of beta distributions to identify significant correlations among $P$ predictors when $P$ is large. The method relies on theorems in convex geometry, which we use to show how to control the error rate of edge detection in graphical models. Our `betaMix' method does not require any assumptions about the network structure, nor
Haim Bar 0001, Martin T. Wells
openaire +3 more sources