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| Bayesianskt blandadeffektsmodell× | Bayesiansk hierarkisk linjär modell× | |
|---|---|---|
| Ämnesområde | Statistik | Statistik |
| Familj | Regression model | Regression model |
| Ursprungsår≠ | 1990s–2000s (modern Bayesian MCMC era) | 2006 |
| Upphovsperson≠ | Gelman, Hill, and the broader Bayesian hierarchical modeling tradition | Gelman & Hill (2006); Raudenbush & Bryk (2002) for frequentist HLM; Bayesian treatment consolidated by Gelman et al. |
| Typ≠ | Bayesian regression model | Bayesian multilevel linear model |
| Ursprungskälla≠ | Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891 | Gelman, A., & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891 |
| Alias | Bayesian multilevel model, Bayesian random effects model, Bayesian LME, Bayesian hierarchical mixed model | Bayesian HLM, Bayesian multilevel linear model, Bayesian random-effects linear model, Bayes hierarchical regression |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | The Bayesian mixed effects model extends the classical mixed effects framework by placing prior distributions on all parameters — fixed effects, random effect variances, and residual variance — and updating them with data to produce full posterior distributions. This provides coherent uncertainty quantification for both population-level and group-level effects simultaneously. | The Bayesian Hierarchical Linear Model (Bayesian HLM) estimates linear relationships in nested or clustered data by placing prior distributions on all model parameters and updating them with observed data. It simultaneously models variation within groups and between groups, propagating uncertainty fully through posterior distributions rather than relying on asymptotic approximations. |
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