Compara mètodes

	Inferencia Bayesiana de Sèries Temporals	Inferència bayesiana jeràrquica
Camp	Bayesià	Bayesià
Família	Bayesian methods	Bayesian methods
Any d'origen	1989	1972 (Lindley & Smith); consolidated 1995–2013
Autor original	Mike West and Jeff Harrison	Lindley & Smith; Gelman et al.
Tipus	Bayesian probabilistic model	Bayesian multilevel model
Font 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
Àlies	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
Relacionats	6	6
Resum	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.