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	Modelowanie hierarchiczne bayesowskie	System Bonus-Malus (BMS)
Dziedzina	Statystyka bayesowska	Nauki aktuarialne
Rodzina	Bayesian methods	Regression model
Rok powstania	2006	1995
Twórca	Gelman & Hill (2006); Bayesian multilevel tradition	Jean Lemaire
Typ	hierarchical probabilistic model	Actuarial experience-rating model
Źródło pierwotne	Gelman, A. & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.	Lemaire, J. (1995). Bonus-Malus Systems in Automobile Insurance. Kluwer Academic Publishers. ISBN: 978-0-7923-9545-5
Inne nazwy	multilevel Bayes, Bayesian multilevel model, Bayesian HLM, partial pooling model	No-Claim Discount System, Merit Rating System, Experience Rating in Automobile Insurance, Prim-Ceza Sistemi
Pokrewne	4	2
Podsumowanie	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.	A Bonus-Malus System (BMS) is an actuarial experience-rating mechanism used primarily in automobile insurance to adjust individual policyholders' premiums based on their personal claim history. Policyholders who remain claim-free receive premium discounts (bonus), while those who file claims are penalised with surcharges (malus). The framework was comprehensively formalised and analysed by Jean Lemaire in his landmark 1995 monograph, which remains the definitive reference for the design and evaluation of such systems worldwide.