Bayesian analysis of cross-sectional networks: A tutorial in R and JASP
Network psychometrics is a new direction in psychological research that conceptualizes
psychological constructs as systems of interacting variables. In network analysis, variables …
psychological constructs as systems of interacting variables. In network analysis, variables …
BDgraph: An R package for Bayesian structure learning in graphical models
R Mohammadi, EC Wit - arXiv preprint arXiv:1501.05108, 2015 - arxiv.org
Graphical models provide powerful tools to uncover complicated patterns in multivariate
data and are commonly used in Bayesian statistics and machine learning. In this paper, we …
data and are commonly used in Bayesian statistics and machine learning. In this paper, we …
Objective Bayesian edge screening and structure selection for Ising networks
The Ising model is one of the most widely analyzed graphical models in network
psychometrics. However, popular approaches to parameter estimation and structure …
psychometrics. However, popular approaches to parameter estimation and structure …
Information enhanced model selection for Gaussian graphical model with application to metabolomic data
J Zhou, AG Hoen, S Mcritchie, W Pathmasiri… - …, 2022 - academic.oup.com
In light of the low signal-to-noise nature of many large biological data sets, we propose a
novel method to learn the structure of association networks using Gaussian graphical …
novel method to learn the structure of association networks using Gaussian graphical …
Objective Bayesian Edge Screening and Structure Selection for Ising Networks
The Ising model is one of the most widely analyzed graphical models in network
psychometrics. However, popular approaches to parameter estimation and structure …
psychometrics. However, popular approaches to parameter estimation and structure …