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Support Recovery of Gaussian Graphical Model with False Discovery Rate Control
This paper focuses on the support recovery of the Gaussian graphical model (GGM) with false discovery rate (FDR) control. The graceful symmetrized...
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Robustly Fitting Gaussian Graphical Models—the R Package robFitConGraph
This chapter gives a tutorial-style introduction to the R package robFitConGraph, which provides a robust goodness-of-fit test for Gaussian graphical...
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The structure of psychosocial factors in academic success: A gaussian graphical model approach
Past research identified various psychosocial indicators of college students’ academic success. Using the affordance ecology framework, the present...
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Unbalanced distributed estimation and inference for the precision matrix in Gaussian graphical models
This paper studies the estimation of Gaussian graphical models in the unbalanced distributed framework. It provides an effective approach when the...
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Bayesian Graphical Modeling with the Circular Drift Diffusion Model
The circular drift-diffusion model (CDDM) is a sequential sampling model designed to account for decisions and response times in decision-making...
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Nonparametric dynamics modeling for underwater vehicles using local adaptive moment estimation Gaussian processes learning
This paper investigates a nonparametric modeling scheme for underwater vehicles to achieve continuous-time dynamics modeling, which is essential for...
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Path-level interpretation of Gaussian graphical models using the pair-path subscore
BackgroundConstruction of networks from cross-sectional biological data is increasingly common. Many recent methods have been based on Gaussian...
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Neural Graphical Models
Probabilistic Graphical Models are often used to understand dynamics of a system. They can model relationships between features (nodes) and the...
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Dynamic and robust Bayesian graphical models
Gaussian graphical models are widely popular for studying the conditional dependence among random variables. By encoding conditional dependence as an...
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GMMchi: gene expression clustering using Gaussian mixture modeling
BackgroundCancer evolution consists of a stepwise acquisition of genetic and epigenetic changes, which alter the gene expression profiles of cells in...
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Multiclass Sparse Discriminant Analysis Incorporating Graphical Structure Among Predictors
In the era of big data, many sparse linear discriminant analysis methods have been proposed for classification and variable selection of the...
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Dietary networks identified by Gaussian graphical model and general and abdominal obesity in adults
BackgroundGaussian graphical model (GGM) has been introduced as a new approach to identify patterns of dietary intake. We aimed to investigate the...
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Two Gaussian Regularization Methods for Time-Varying Networks
We model time-varying network data as realizations from multivariate Gaussian distributions with precision matrices that change over time. To...
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Variational Gaussian topic model with invertible neural projections
Neural topic models have triggered a surge of interest in extracting topics from text automatically since they avoid the sophisticated derivations in...
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Gaussian graphical modeling of the serum exposome and metabolome reveals interactions between environmental chemicals and endogenous metabolites
Given the complex exposures from both exogenous and endogenous sources that an individual experiences during life, exposome-wide association studies...
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Gaussian Processes
The Gaussian process as a tool for, predominantly, regression tasks in machine learning has only been growing in popularity over recent years....
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A comprehensive review of Gaussian atmospheric dispersion models: current usage and future perspectives
AbstractThis article provides an in-depth review of Gaussian atmospheric dispersion models, which are mathematical tools used to predict the...
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Graphical Models
The beginning of this book illustrates that linear regression models can describe the relationships between the genes’ copy numbers and a biomarker....
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Block Structured Graph Priors in Gaussian Graphical Models
Colombi, AlessandroGaussian graphical models are a powerful statistical tool to describe the concept of conditional independence between variables...
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Recent advances in 3D Gaussian splatting
The emergence of 3D Gaussian splatting (3DGS) has greatly accelerated rendering in novel view synthesis. Unlike neural implicit representations like...
