Opaque Prior Distributions in Bayesian Latent Variable Models
Abstract: We review common situations in Bayesian latent variable models where the prior distribution that a researcher specifies differs from the prior distribution used during estimation. These situations can arise from the positive definite requirement on correlation matrices, from sign indeterminacy of factor loadings, and from order constraints on threshold parameters. The issue is especially problematic for reproducibility and for model checks that involve prior distributions, including prior predictive assessment and Bayes factors. In these cases, one might be assessing the wrong model, casting doubt on the relevance of the results. The most straightforward solution to the issue sometimes involves use of informative prior distributions. We explore other solutions and make recommendations for practice. https://meth.psychopen.eu/index.php/meth/article/view/11167
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Opaque Prior Distributions in Bayesian Latent Variable Models ; volume:19 ; number:3 ; day:29 ; month:09 ; year:2023
Methodology ; 19, Heft 3 (29.09.2023)
- Creator
-
Merkle, Edgar C.
Ariyo, Oludare
Winter, Sonja D.
Garnier-Villarreal, Mauricio
- DOI
-
10.5964/meth.11167
- URN
-
urn:nbn:de:101:1-2023093005100431656935
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:53 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Merkle, Edgar C.
- Ariyo, Oludare
- Winter, Sonja D.
- Garnier-Villarreal, Mauricio
Other Objects (12)
Optimal Bayesian estimators for latent variable cluster models
A geoadditive Bayesian latent variable model for Poisson indicators
A Bayesian semiparametric latent variable model for mixed responses
Nonparametric Bayesian discrete latent variable models for unsupervised learning
Networks as mediating variables: a Bayesian latent space approach
Bayesian modeling of ChIP-chip data using latent variables
Hybrid Bayesian network discovery with latent variables by scoring multiple interventions
Bayesian Analysis of Two-Part Latent Variable Model with Mixed Data
Opaque Options
Bayesian estimation of latent trait distributions considering hierarchical structures and partially missing covariate data
Maize opaque mutants are no longer so opaque
Modeling Epistemic and Aleatoric Uncertainty with Bayesian Neural Networks and Latent Variables
Optimal Bayesian estimators for latent variable cluster models
A geoadditive Bayesian latent variable model for Poisson indicators
A Bayesian semiparametric latent variable model for mixed responses
Nonparametric Bayesian discrete latent variable models for unsupervised learning
Networks as mediating variables: a Bayesian latent space approach
Bayesian modeling of ChIP-chip data using latent variables
Hybrid Bayesian network discovery with latent variables by scoring multiple interventions
Bayesian Analysis of Two-Part Latent Variable Model with Mixed Data
Opaque Options
Bayesian estimation of latent trait distributions considering hierarchical structures and partially missing covariate data
Maize opaque mutants are no longer so opaque
Modeling Epistemic and Aleatoric Uncertainty with Bayesian Neural Networks and Latent Variables
Optimal Bayesian estimators for latent variable cluster models
A geoadditive Bayesian latent variable model for Poisson indicators
A Bayesian semiparametric latent variable model for mixed responses
Nonparametric Bayesian discrete latent variable models for unsupervised learning
Networks as mediating variables: a Bayesian latent space approach
Bayesian modeling of ChIP-chip data using latent variables
Hybrid Bayesian network discovery with latent variables by scoring multiple interventions
Bayesian Analysis of Two-Part Latent Variable Model with Mixed Data
Opaque Options
Bayesian estimation of latent trait distributions considering hierarchical structures and partially missing covariate data
Maize opaque mutants are no longer so opaque