APS
2022 APS Annual Convention
Advances in Bayesian Structural Equation Modeling: Bayesian Solutions to Practical Challenges
As Bayesian analysis is increasingly used in structural equation modeling (SEM, additional benefits of it are explored. This symposium will discuss new advances of Bayesian treatment of substantive issues of fitting SEM including model uncertainty and selection, outliers, and omitting confounders in causal inference.
Chairs & Discussants
- Haiyan LiuChair
University of California, Merced - Xin TongCoChair
University of Virginia
Presentations
- Bayesian Methods for Handling Time Uncertainty in Growth Curve ModelingHaiyan Liu
- Using Small-Variance Priors in Latent Class AnalysisMarieke Visser
- Causal Mediation Analysis with Bayesian Informative Priors of the Confounding EffectQian Zhang
- Robust Bayesian Growth Curve Modeling Using Conditional QuantilesXin Tong
