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| Idősoros Bayes-féle következtetés× | Hierarchikus Bayes-féle következtetés× | |
|---|---|---|
| Tudományterület | Bayes-statisztika | Bayes-statisztika |
| Módszercsalád | Bayesian methods | Bayesian methods |
| Keletkezés éve≠ | 1989 | 1972 (Lindley & Smith); consolidated 1995–2013 |
| Megalkotó≠ | Mike West and Jeff Harrison | Lindley & Smith; Gelman et al. |
| Típus≠ | Bayesian probabilistic model | Bayesian multilevel model |
| Alapmű≠ | West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259 | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| Alternatív nevek | Bayesian time series analysis, Bayesian state-space modeling, probabilistic time series inference, BSTS | multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model |
| Kapcsolódó | 6 | 6 |
| Összefoglaló≠ | Time series Bayesian inference applies Bayes' theorem sequentially to time-ordered observations, maintaining a full probability distribution over hidden states and model parameters at every time step. This framework unifies state-space models, dynamic linear models, and particle filters, producing calibrated uncertainty for both filtering (real-time) and retrospective smoothing tasks. | Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate. |
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