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| Bayesian Hierarchicales Zeitreihenmodell× | Mehrstufige Bayes'sche Inferenz× | |
|---|---|---|
| Fachgebiet | Bayes-Statistik | Bayes-Statistik |
| Familie | Bayesian methods | Bayesian methods |
| Entstehungsjahr≠ | 1989–1997 | 1980s–2000s |
| Urheber≠ | West & Harrison (dynamic models); Gelman et al. (hierarchical Bayesian framework) | Gelman, Hill, Raudenbush, Bryk |
| Typ≠ | Bayesian hierarchical model for time series | Bayesian hierarchical model |
| Wegweisende Quelle≠ | West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259 | Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891 |
| Aliasnamen | TSBHM, Bayesian hierarchical time series, hierarchical dynamic Bayesian model, multilevel Bayesian time series | Bayesian multilevel model, Bayesian hierarchical model, Bayesian mixed-effects model, Bayesian random-effects model |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | A time series Bayesian hierarchical model combines the hierarchical (multilevel) Bayesian framework with a dynamic state-space structure to analyse temporal data collected on multiple units or groups. Priors encode beliefs about both within-unit dynamics and cross-unit variation, and the posterior is obtained via MCMC or sequential Monte Carlo, yielding full probabilistic forecasts with calibrated uncertainty. | Multilevel Bayesian inference combines Bayesian probability with hierarchical data structures, treating group-level parameters as drawn from a common population distribution. It simultaneously estimates unit-level effects and the hyperparameters governing their variation, propagating full uncertainty through every level of the hierarchy via posterior sampling. |
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