Results 31 to 40 of about 22,057,993 (342)
Journal of Machine Learning Research, 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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Tests for Gaussian graphical models [PDF]
Computational Statistics & Data Analysis, 2009 Gaussian graphical models are promising tools for analysing genetic networks. In many applications, biologists have some knowledge of the genetic network and may want to assess the quality of their model using gene expression data. This is why one introduces a novel procedure for testing the neighborhoods of a Gaussian graphical model.
Verzelen, Nicolas, Villers, Fanny
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Generalized Permutohedra from Probabilistic Graphical Models [PDF]
, 2017 A graphical model encodes conditional independence relations via the Markov properties. For an undirected graph these conditional independence relations can be represented by a simple polytope known as the graph associahedron, which can be constructed as
Caroline Uhler +13 more
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Conditional Random Field-Guided Multi-Focus Image Fusion
Journal of Imaging, 2022 Multi-Focus image fusion is of great importance in order to cope with the limited Depth-of-Field of optical lenses. Since input images contain noise, multi-focus image fusion methods that support denoising are important.
Odysseas Bouzos +2 more
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A Nonparametric Graphical Model for Functional Data With Application to Brain Networks Based on fMRI
Journal of the American Statistical Association, 2018 We introduce a nonparametric graphical model whose observations on vertices are functions. Many modern applications, such as electroencephalogram and functional magnetic resonance imaging (fMRI), produce data are of this type.
Bing Li, Eftychia Solea
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Modeling Categorical Variables by Mutual Information Decomposition
Entropy, 2023 This paper proposed the use of mutual information (MI) decomposition as a novel approach to identifying indispensable variables and their interactions for contingency table analysis.
Jiun-Wei Liou +2 more
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Graphical Log-linear Models: Fundamental Concepts and Applications [PDF]
, 2016 We present a comprehensive study of graphical log-linear models for contingency tables. High dimensional contingency tables arise in many areas such as computational biology, collection of survey and census data and others. Analysis of contingency tables
Gauraha, Niharika
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Modeling the Game of Go by Ising Hamiltonian, Deep Belief Networks and Common Fate Graphs
IEEE Access, 2019 Three different models of the game of Go are developed by establishing an analogy between this game and physical systems susceptible to analysis under the well-known Ising model in two dimensions. The Ising Hamiltonian is adapted to measure the energy of
Alfonso Rojas-Dominguez +2 more
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Graphical Notation for Document Database Modeling
Открытое образование (Москва), 2021 Goals and objectives. Graphical models have proven to be a reliable, clear and convenient tool for creating sketch models of databases. Most of the existing notations are designed for the relational data model, the dominant data model for the last thirty
M. V. Smirnov, R. S. Tolmasov
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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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