Partial correlation hypersurfaces in Gaussian graphical models
نویسندگان
چکیده
منابع مشابه
Equivalent Partial Correlation Selection for High Dimensional Gaussian Graphical Models
Gaussian graphical models (GGMs) are frequently used to explore networks, such as gene regulatory networks, among a set of variables. Under the classical theory of GGMs, the graph construction amounts to finding the pairs of variables with nonzero partial correlation coefficients. However, this is infeasible for high dimensional problems for which the number of variables is larger than the samp...
متن کاملCopula Gaussian Graphical Models *
We propose a comprehensive Bayesian approach for graphical model determination in observational studies that can accommodate binary, ordinal or continuous variables simultaneously. Our new models are called copula Gaussian graphical models and embed graphical model selection inside a semiparametric Gaussian copula. The domain of applicability of our methods is very broad and encompass many stud...
متن کاملTesting Unfaithful Gaussian Graphical Models
The global Markov property for Gaussian graphical models ensures graph separation implies conditional independence. Specifically if a node set S graph separates nodes u and v then Xu is conditionally independent of Xv given XS . The opposite direction need not be true, that is, Xu ⊥ Xv | XS need not imply S is a node separator of u and v. When it does, the relation Xu ⊥ Xv | XS is called faithf...
متن کاملAdaptive Sparsity in Gaussian Graphical Models
An effective approach to structure learning and parameter estimation for Gaussian graphical models is to impose a sparsity prior, such as a Laplace prior, on the entries of the precision matrix. Such an approach involves a hyperparameter that must be tuned to control the amount of sparsity. In this paper, we introduce a parameter-free method for estimating a precision matrix with sparsity that ...
متن کاملFlexible Covariance Estimation in Graphical Gaussian Models
In this paper, we propose a class of Bayes estimators for the covariance matrix of graphical Gaussian models Markov with respect to a decomposable graph G. Working with the WPG family defined by Letac and Massam [Ann. Statist. 35 (2007) 1278–1323] we derive closed-form expressions for Bayes estimators under the entropy and squared-error losses. The WPG family includes the classical inverse of t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Algebraic Combinatorics
سال: 2019
ISSN: 2589-5486
DOI: 10.5802/alco.44