APS
29th APS Annual Convention
<span>Modeling Real-World Complexity Using Bayesian Methods</Span>
Bayesian estimation routines provide distinct advantages over classical (i.e., frequentist) methods for estimating and drawing inferences about parameters. We provide three demonstrations of how a Bayesian framework elegantly handles multiple correlated influences on human behavior—often unobserved (missing or latent)—which would be difficult or intractable using classical methods.
Chairs & Discussants
- Terrence JorgensenChair
University of Amsterdam
Presentations
- Bayesian Mixed-Effects Nonstationary Latent Differential Equation Model (BMNLDE): An Application to Accelerometer DataMauricio Garnier-Villarreal, Amber Watts
- Social Relations Model of Self- and Peer-Perceived Body Preoccupation: Modeling Missing Data in an Open NetworkTerrence Jorgensen, K. Forney, Jeffrey Hall
- Using a Bayesian Multilevel Graded Response Model to Detect Group Differences in Measurement Properties of Grit Scale Items Across SchoolsGraham Rifenbark, Allison Lombardi, Jennifer Freeman
