Bayesian hypothesis testing for Gaussian graphical models: Conditional independence and order constraints
Donald R. Williams,Joris Mulder +1 more
Reads0
Chats0
TLDR
This work introduces exploratory and confirmatory Bayesian tests for partial correlations in Gaussian graphical models and describes the novel matrix- F prior distribution that provides increased flexibility in specification compared to the Wishart prior.About:
This article is published in Journal of Mathematical Psychology.The article was published on 2020-12-01 and is currently open access. It has received 27 citations till now. The article focuses on the topics: Conditional independence & Graphical model.read more
Citations
More filters
Journal ArticleDOI
The narcissism network and centrality of narcissism features
TL;DR: In this article, the authors explored the underlying structural organization and centrality of narcissism features by using network analysis and found that although features of grandiose narcissism (grandiose exhibitionism and leadership/authority) show consistent higher strength centrality across networks, entitlement/exploitativeness has a central role in bringing together maladaptive and vulnerable aspects.
Journal ArticleDOI
Describing Disclosure of Cybervictimization in Adolescents from the United Kingdom: The Role of Age, Gender, Involvement in Cyberbullying, and Time Spent Online.
TL;DR: For example, this article found that over 88% of the participants reported that they would disclose cyber-victimization and over 80% thought friends would be helpful following cyber-bullying disclosure, whereas those who were younger reported that parents and the police would be useful.
Journal ArticleDOI
Gaussian graphical models with applications to omics analyses
TL;DR: An overview of GGM theory and a demonstration of various GGM tools in R are provided, emphasizing methods recently developed for high‐dimensional applications such as genomics, proteomics, or metabolomics.
Journal ArticleDOI
Feature optimization method for the localization technology on loose particles inside sealed electronic equipment
TL;DR: In this article , a multi-channel weighted threshold feature selection method was proposed for the localization of loose particles inside sealed electronic equipment, and the results showed that the plane and spatial localization accuracy achieved on the localization dataset increased from 87.01% and 83.67% to 95.23% and 95.51%, respectively.
Journal ArticleDOI
Bayesian Bootstrapped Correlation Coefficients
TL;DR: The Bayesian bootstrap is proposed as a generic, simple, and accessible method for sampling from the posterior distribution of various correlation coefficients that are commonly used in the social-behavioral sciences.
References
More filters
Journal ArticleDOI
A power primer.
TL;DR: A convenient, although not comprehensive, presentation of required sample sizes is providedHere the sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests.
Book
Bayesian Data Analysis
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
Journal ArticleDOI
Tests for comparing elements of a correlation matrix.
TL;DR: This article reviewed the literature on such tests, pointed out some statistics that should be avoided, and presented a variety of techniques that can be used safely with medium to large samples, and several illustrative numerical examples are provided.
Journal ArticleDOI
High-dimensional graphs and variable selection with the Lasso
TL;DR: It is shown that neighborhood selection with the Lasso is a computationally attractive alternative to standard covariance selection for sparse high-dimensional graphs and is hence equivalent to variable selection for Gaussian linear models.
Journal ArticleDOI
Network Analysis: An Integrative Approach to the Structure of Psychopathology
TL;DR: An examines methodologies suited to identify such symptom networks and discusses network analysis techniques that may be used to extract clinically and scientifically useful information from such networks (e.g., which symptom is most central in a person's network).