Gaussian graphical models with applications to omics analyses
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9546
Abstract: Gaussian graphical models (GGMs) provide a framework for modeling conditional dependencies in multivariate data. In this tutorial, we provide an overview of GGM theory and a demonstration of various GGM tools in R. The mathematical… read more here.
Keywords: gaussian graphical; omics analyses; applications omics; models applications ... See more keywords
Probabilistic Graphical Models Applied to Biological Networks.
Sign Up to like & getrecommendations! Published in 2021 at "Advances in experimental medicine and biology"
DOI: 10.1007/978-3-030-80352-0_7
Abstract: Biological networks can be defined as a set of molecules and all the interactions among them. Their study can be useful to predict gene function, phenotypes, and regulate molecular patterns. Probabilistic graphical models (PGMs) are… read more here.
Keywords: probabilistic graphical; graphical models; models applied; applied biological ... See more keywords
Experiments with learning graphical models on text
Sign Up to like & getrecommendations! Published in 2018 at "Behaviormetrika"
DOI: 10.1007/s41237-018-0050-3
Abstract: A rich variety of models are now in use for unsupervised modelling of text documents, and, in particular, a rich variety of graphical models exist, with and without latent variables. To date, there is inadequate… read more here.
Keywords: models text; experiments learning; matrix factorisation; learning graphical ... See more keywords
Bayesian hypothesis testing for Gaussian graphical models: Conditional independence and order constraints
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Mathematical Psychology"
DOI: 10.1016/j.jmp.2020.102441
Abstract: Abstract Gaussian graphical models (GGM; partial correlation networks) have become increasingly popular in the social and behavioral sciences for studying conditional (in)dependencies between variables. In this work, we introduce exploratory and confirmatory Bayesian tests for… read more here.
Keywords: hypothesis; graphical models; conditional independence; gaussian graphical ... See more keywords
Process monitoring using causal graphical models, with application to clogging detection in steel continuous casting
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Process Control"
DOI: 10.1016/j.jprocont.2021.08.006
Abstract: Abstract The availability of manufacturing data is expected to grow exponentially due to the accelerating advancement in information technology, smart sensing, and industrial internet of things. To be able to efficiently leverage industrial “big data”… read more here.
Keywords: process monitoring; graphical models; process; causal graphical ... See more keywords
Directed functional connectivity using dynamic graphical models
Sign Up to like & getrecommendations! Published in 2018 at "Neuroimage"
DOI: 10.1016/j.neuroimage.2018.03.074
Abstract: &NA; There are a growing number of neuroimaging methods that model spatio‐temporal patterns of brain activity to allow more meaningful characterizations of brain networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional… read more here.
Keywords: dynamic graphical; network; directed functional; graphical models ... See more keywords
Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis
Sign Up to like & getrecommendations! Published in 2020 at "Neuropsychologia"
DOI: 10.1016/j.neuropsychologia.2020.107500
Abstract: With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent years. Aggregating these data to derive scientific insights often faces… read more here.
Keywords: incorporating structured; fmri data; assumptions probabilistic; structured assumptions ... See more keywords
Pairwise graphical models for structural health monitoring with dense sensor arrays
Sign Up to like & getrecommendations! Published in 2017 at "Mechanical Systems and Signal Processing"
DOI: 10.1016/j.ymssp.2017.02.026
Abstract: Abstract Through advances in sensor technology and development of camera-based measurement techniques, it has become affordable to obtain high spatial resolution data from structures. Although measured datasets become more informative by increasing the number of… read more here.
Keywords: health monitoring; structural health; dense sensor; sensor ... See more keywords
Cluster Gelnet for estimating Gaussian graphical models with multi-level conditional correlations and block structures
Sign Up to like & getrecommendations! Published in 2025 at "Journal of Statistical Computation and Simulation"
DOI: 10.1080/00949655.2025.2566413
Abstract: This paper introduces a two-step method to exploit hidden block structures in Gaussian graphical models, achieving improved accuracy and scalability with theoretical guarantees and strong empirical performance. This paper addresses the estimation of Gaussian graphical… read more here.
Keywords: cluster gelnet; gaussian graphical; graphical models; block structures ... See more keywords
Functional Graphical Models
Sign Up to like & getrecommendations! Published in 2019 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2017.1390466
Abstract: ABSTRACT Graphical models have attracted increasing attention in recent years, especially in settings involving high-dimensional data. In particular, Gaussian graphical models are used to model the conditional dependence structure among multiple Gaussian random variables. As… read more here.
Keywords: high dimensional; conditional dependence; functional graphical; dependence structure ... See more keywords
Handbook of Graphical Models
Sign Up to like & getrecommendations! Published in 2020 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2020.1801279
Abstract: Graphical models represent probability distributions as a graph with edges denoting conditional dependence relationships between random variables. These models have been studied and developed in co... read more here.
Keywords: handbook graphical; graphical models;