Salīdzināt metodes

	Bayesiskā hierarhiskā modelēšana	Svēršanas un kalibrēšanas aptaujas
Nozare	Bajesa metodes	Aptauju metodoloģija
Saime	Bayesian methods	Process / pipeline
Izcelsmes gads	2006	2010
Autors	Gelman & Hill (2006); Bayesian multilevel tradition	Sharon Lohr
Tips	hierarchical probabilistic model	Estimation adjustment procedure
Pirmavots	Gelman, A. & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.	Lohr, S. L. (2010). Sampling: Design and Analysis (2nd ed.). Brooks/Cole. ISBN: 978-0-495-10527-5
Citi nosaukumi	multilevel Bayes, Bayesian multilevel model, Bayesian HLM, partial pooling model	Survey Calibration, Post-Stratification Weighting, Raking Adjustment, Ağırlıklandırma (Anket)
Saistītās	4	3
Kopsavilkums	Bayesian hierarchical modelling, popularised by Gelman and Hill (2006), is a Bayesian approach to nested data structures — such as students within schools within districts — that estimates separate parameters at each level while allowing those levels to share statistical strength through a mechanism called partial pooling. Where a classical hierarchical linear model treats group means as fixed unknown quantities, the Bayesian version places hyperprior distributions on those group means so that information flows freely across levels, producing more reliable group-level estimates whenever any individual group has few observations.	Survey weighting is a statistical procedure that assigns a numeric weight to each sampled unit so that the weighted sample reproduces known population totals. Rooted in classical sampling theory and systematically synthesized by Sharon Lohr (2010), the approach corrects for unequal selection probabilities, unit nonresponse, and coverage gaps, producing estimates that are more representative of the target population than raw sample means or totals would be.