Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Inferencia bayesiana de series temporales× | Inferencia Bayesiana Jerárquica× | |
|---|---|---|
| Campo | Bayesiano | Bayesiano |
| Familia | Bayesian methods | Bayesian methods |
| Año de origen≠ | 1989 | 1972 (Lindley & Smith); consolidated 1995–2013 |
| Autor original≠ | Mike West and Jeff Harrison | Lindley & Smith; Gelman et al. |
| Tipo≠ | Bayesian probabilistic model | Bayesian multilevel model |
| Fuente seminal≠ | 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 |
| Alias | 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 |
| Relacionados | 6 | 6 |
| Resumen≠ | 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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