Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 1 |
| Since 2016 (last 10 years) | 2 |
| Since 2006 (last 20 years) | 2 |
Descriptor
Source
| Grantee Submission | 2 |
Author
| Goodrich, Ben | 2 |
| Betancourt, Michael | 1 |
| Brubaker, Marcus A. | 1 |
| Carpenter, Bob | 1 |
| Fitzsimmons, Ellen | 1 |
| Gelman, Andrew | 1 |
| Guo, Jiqiang | 1 |
| Hoffman, Matthew D. | 1 |
| Lee, Daniel | 1 |
| Li, Peter | 1 |
| Merkle, Edgar C. | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 2 |
| Reports - Descriptive | 2 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full text
Direct linkMerkle, Edgar C.; Fitzsimmons, Ellen; Uanhoro, James; Goodrich, Ben – Grantee Submission, 2021
Structural equation models comprise a large class of popular statistical models, including factor analysis models, certain mixed models, and extensions thereof. Model estimation is complicated by the fact that we typically have multiple interdependent response variables and multiple latent variables (which may also be called random effects or…
Descriptors: Bayesian Statistics, Structural Equation Models, Psychometrics, Factor Analysis
Carpenter, Bob; Gelman, Andrew; Hoffman, Matthew D.; Lee, Daniel; Goodrich, Ben; Betancourt, Michael; Brubaker, Marcus A.; Guo, Jiqiang; Li, Peter; Riddell, Allen – Grantee Submission, 2017
Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the…
Descriptors: Programming Languages, Probability, Bayesian Statistics, Monte Carlo Methods
