Results 11 to 20 of about 418,262 (313)
, 2023 This graphical model represents the dependencies between the states, observations and the randomly picked peptide distribution in our model.
Joakim Jaldén (17350753) +1 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 ...
Marieta Georgieva Ivanova +3 more
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, 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
Harsh Shrivastava 0001 +1 more
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Vehicles, 2023 Computer vehicle simulators are used to model real-world situations to overcome time and cost limitations. The vehicle simulators provide virtual scenarios for real-world driving.
Su Man Nam +4 more
doaj +1 more source
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
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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
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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
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Explicit Formula of Koszul–Vinberg Characteristic Functions for a Wide Class of Regular Convex Cones
Entropy, 2016 The Koszul–Vinberg characteristic function plays a fundamental role in the theory of convex cones. We give an explicit description of the function and related integral formulas for a new class of convex cones, including homogeneous cones and cones ...
Hideyuki Ishi
doaj +1 more source