Порівняння методів

	Байєсівська ієрархічна модель	Система "бонус-малус" (СБМ)
Галузь	Баєсові методи	Актуарна наука
Родина	Bayesian methods	Regression model
Рік появи	2006	1995
Автор методу	Gelman & Hill (2006); Bayesian multilevel tradition	Jean Lemaire
Тип	hierarchical probabilistic model	Actuarial experience-rating model
Основоположне джерело	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
Інші назви	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
Пов'язані	4	2
Підсумок	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.