Σύγκριση μεθόδων

	Θεωρία Πιστοληπτικότητας	Μπεϋζιανή Ιεραρχική Μοντελοποίηση	Σύστημα Bonus-Malus	Μοντέλο Κατανομής Απωλειών
Πεδίο	Αναλογιστική Επιστήμη	Μπεϋζιανή Στατιστική	Αναλογιστική Επιστήμη	Αναλογιστική Επιστήμη
Οικογένεια	Regression model	Bayesian methods	Regression model	Regression model
Έτος προέλευσης	1967	2006	1995	2012
Δημιουργός	Hans Bühlmann	Gelman & Hill (2006); Bayesian multilevel tradition	Jean Lemaire	Klugman, Panjer & Willmot
Τύπος	Weighted linear blend of individual and collective experience	hierarchical probabilistic model	Actuarial experience-rating model	Parametric probability model
Θεμελιώδης πηγή	Bühlmann, H. (1967). Experience rating and credibility. ASTIN Bulletin, 4(3), 199–207.	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	Klugman, S. A., Panjer, H. H., & Willmot, G. E. (2012). Loss Models: From Data to Decisions (4th ed.). Wiley. ISBN: 978-1-118-31532-3
Εναλλακτικές ονομασίες	Bühlmann Credibility, Experience Rating, Linear Credibility Estimator, Güvenilirlik Teorisi	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	Severity-Frequency Model, Aggregate Loss Model, Claim Size Distribution Model, Hasar Dağılımı Modeli
Συναφείς	3	4	2	3
Σύνοψη	Credibility Theory is an actuarial framework for estimating the pure premium of an individual risk by blending its own observed loss experience with the collective (portfolio) mean. Introduced by Hans Bühlmann in 1967, the method derives the optimal linear combination—the credibility-weighted premium—that minimises mean squared error. It extends classical experience rating to a rigorous statistical footing rooted in Bayesian and linear estimation principles.	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.	A Loss Distribution Model is a parametric statistical framework used in actuarial science to characterise the probabilistic behaviour of insurance claim amounts and frequencies. Developed comprehensively by Klugman, Panjer, and Willmot in their foundational text Loss Models: From Data to Decisions (first edition 1998, fourth edition 2012), these models underpin premium rating, reserving, reinsurance pricing, and regulatory capital calculations across the insurance and risk-management industries.