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| Multilevel Bayesian Model Averaging× | Multilevel Variational Inference× | |
|---|---|---|
| Ämnesområde | Bayesiansk statistik | Bayesiansk statistik |
| Familj | Bayesian methods | Bayesian methods |
| Ursprungsår≠ | 1999–2000s | 2016 |
| Upphovsperson≠ | Hoeting, Madigan, Raftery, Volinsky (BMA foundation); multilevel extension developed across the late 1990s–2000s | Ranganath, Altosaar, Tran, Blei (hierarchical VI formalization, 2016); Blei et al. (VI framework, 2017) |
| Typ≠ | Bayesian ensemble / model selection | approximate Bayesian inference |
| Ursprungskälla≠ | Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-401. link ↗ | Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859-877. DOI ↗ |
| Alias | ML-BMA, hierarchical Bayesian model averaging, multilevel BMA, Bayesian model averaging in multilevel models | hierarchical variational inference, multilevel VI, variational Bayes for multilevel models, MLVI |
| Närliggande≠ | 6 | 4 |
| Sammanfattning≠ | Multilevel Bayesian model averaging (ML-BMA) extends classical Bayesian model averaging to grouped or hierarchically structured data. Rather than committing to a single multilevel model specification, it computes a weighted average of predictions and parameter estimates across a set of candidate multilevel models, weighting each model by its posterior probability given the data. The result accounts simultaneously for uncertainty in the grouping structure, fixed effects, random effects, and covariate selection. | Multilevel variational inference (MLVI) is a scalable approximate Bayesian method that fits hierarchical (multilevel) models by optimizing a variational approximation to the posterior, rather than drawing MCMC samples. It exploits the grouped structure of multilevel data — individuals nested within groups, groups nested within higher-level units — to derive efficient coordinate-wise updates, making Bayesian inference tractable for large clustered datasets. |
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