Results 31 to 40 of about 22,758,545 (367)
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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Mathematics, 2022 Strong exogeneity is an important assumption in the study of causal inference, but it is difficult to identify according to its definition. The twin network method provides a graphical model tool for analyzing the variable relationship, involving the ...
Rui Luo +4 more
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Axioms, 2022 This paper shows a visual analysis and the dependence relationships of COVID-19 mortality data in 50 states plus Washington, D.C., from January 2020 to 1 September 2022.
Jong-Min Kim
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Discrete graphical models -- an optimization perspective [PDF]
Foundations and Trends in Computer Graphics and Vision: Vol. 11:
No. 3-4, pp 160-429 (2019), 2020 This monograph is about discrete energy minimization for discrete graphical models. It considers graphical models, or, more precisely, maximum a posteriori inference for graphical models, purely as a combinatorial optimization problem. Modeling, applications, probabilistic interpretations and many other aspects are either ignored here or find their ...
arxiv +1 more source
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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Cavity approximation for graphical models [PDF]
Physical Review E, 2007 Extension to factor graphs and comments on related work ...
Rizzo, T. +3 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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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
semanticscholar +1 more source
Frontiers in Genetics, 2019 High-throughput microbial sequencing techniques, such as targeted amplicon-based and metagenomic profiling, provide low-cost genomic survey data of microbial communities in their natural environment, ranging from marine ecosystems to host-associated ...
Grace Yoon +2 more
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