Bandingkan metode

	Inferensi Bayesian Dinamis	Inferensi Bayesian Hierarkis
Bidang	Bayesian	Bayesian
Keluarga	Bayesian methods	Bayesian methods
Tahun asal	1989–1997	1972 (Lindley & Smith); consolidated 1995–2013
Pencetus	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)	Lindley & Smith; Gelman et al.
Tipe	Bayesian sequential / online inference framework	Bayesian multilevel model
Sumber perintis	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	online Bayesian inference, sequential Bayesian updating, recursive Bayesian estimation, dynamic Bayesian updating	multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model
Terkait	6	6
Ringkasan	Dynamic Bayesian inference is a framework for performing Bayesian updating sequentially as new observations arrive over time. Rather than fitting a static model to a fixed dataset, it tracks how a posterior distribution over latent states or parameters evolves step by step, combining a prior with each new likelihood to produce an updated posterior that propagates forward through time.	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.