Vertaile menetelmiä

	Robustin Bayesiläinen päättely	Hierarkkinen Bayesiläinen päättely
Tieteenala	Bayesilainen tilastotiede	Bayesilainen tilastotiede
Menetelmäperhe	Bayesian methods	Bayesian methods
Syntyvuosi	1984–1990	1972 (Lindley & Smith); consolidated 1995–2013
Kehittäjä	James O. Berger	Lindley & Smith; Gelman et al.
Tyyppi	Bayesian sensitivity / robustness framework	Bayesian multilevel model
Alkuperäislähde	Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328.	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
Rinnakkaisnimet	Bayesian sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes	multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model
Liittyvät	6	6
Tiivistelmä	Robust Bayesian inference extends standard Bayesian analysis by replacing a single prior distribution with a class of plausible priors and examining how much the posterior conclusions change across that class. Instead of committing to one prior, the analyst bounds the posterior quantity of interest, revealing whether findings are stable or critically dependent on prior assumptions.	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.