Bayesian hypothesis testing for Gaussian graphical models: Conditional independence and order constraints
Donald R. Williams,Joris Mulder +1 more
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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
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Network analysis of multivariate data in psychological science
Denny Borsboom,Marie K. Deserno,Mijke Rhemtulla,Sacha Epskamp,Eiko I. Fried,Richard J. McNally,Donald J. Robinaugh,Marco Perugini,Jonas Dalege,Giulio Costantini,Adela-Maria Isvoranu,Anna C. Wysocki,Claudia D. van Borkulo,Riet van Bork,Lourens J. Waldorp +14 more
TL;DR: This Primer provides an anatomy of network analysis techniques, describes the current state of the art and discusses open problems, as well as assessment techniques to evaluate network robustness and replicability.
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On Nonregularized Estimation of Psychological Networks.
TL;DR: This article describes the glasso method in the context of the fields where it was developed, and demonstrates that the advantages of regularization diminish in settings where psychological networks are often fitted, and introduces nonregularized methods based on multiple regression and a nonparametric bootstrap strategy.
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The case for formal methodology in scientific reform
TL;DR: A formal statistical analysis of three popular claims in the metascientific literature is presented, showing how the use and benefits of such formalism can inform and shape debates about such methodological claims.
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Quantifying the Reliability and Replicability of Psychopathology Network Characteristics.
TL;DR: The present study compared the existing suite of methods for maximizing and quantifying the stability and consistency of PMRF networks with a set of metrics for directly comparing the detailed network characteristics interpreted in the literature and concluded that the limited reliability of the detailed characteristics of networks observed here is likely to be common in practice, but overlooked by current methods.
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Sampling Variability Is Not Nonreplication: A Bayesian Reanalysis of Forbes, Wright, Markon, and Krueger.
TL;DR: A Bayesian re-analysis that quantifies uncertainty and compares relative evidence for replication and nonreplication versus nonequivalence for each network edge provides a principled roadmap for future assessments of network replicability.
References
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Wishart distributions for decomposable graphs
Gérard Letac,Hélène Massam +1 more
TL;DR: In this article, two families of Wishart distributions, namely the Type I and Type II Wishart distribution, were constructed on the cones QG and PG, and they can be viewed as generalizations of the hyper and hyper inverse Wishart as defined by Dawid and Lauritzen.
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Bayesian Inference for a Covariance Matrix
Thomas J. Leonard,John S. J. Hsu +1 more
TL;DR: In this paper, a flexible class of prior distributions is proposed for the covariance matrix of a multivariate normal distribution, yielding much more general hierarchical and empirical Bayes smoothing and inference, when compared with a conjugate analysis involving an inverted Wishart distribution.
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Consistency of Bayesian procedures for variable selection
TL;DR: In this article, it was shown that for a wide class of prior distributions, including intrinsic priors, the corresponding Bayesian procedure for variable selection in normal regression is consistent in the entire class of normal linear models.
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Mean and variance of ratio estimators used in fluorescence ratio imaging.
G.M.P. van Kempen,L.J. van Vliet +1 more
TL;DR: In this paper, the mean and variance of three estimators for the ratio between two random variables, x and y, are discussed, and the results on the simulated and real-world test images confirm the presented theory.
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Bayesian Structure Learning in Sparse Gaussian Graphical Models
Abdolreza Mohammadi,Ernst Wit +1 more
TL;DR: A novel and efficient Bayesian framework for Gaussian graphical model determination which is a trans-dimensional Markov Chain Monte Carlo (MCMC) approach based on a continuous-time birth-death process and gives a principled and, in practice, sensible approach for structure learning.