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	Bayesian Reliability Analysis	Analiza przeżycia
Dziedzina	Statystyka bayesowska	Statystyka w badaniach
Rodzina	Bayesian methods	Process / pipeline
Rok powstania	2008	1958
Twórca	Bayesian reliability formalized by Hamada, Wilson, Reese & Martz	Edward L. Kaplan and Paul Meier
Typ	Bayesian model for time-to-failure / reliability data	Method
Źródło pierwotne	Hamada, M. S., Wilson, A. G., Reese, C. S., & Martz, H. F. (2008). Bayesian Reliability. Springer Series in Statistics. Springer, New York.	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.
Inne nazwy	Bayesian reliability, Bayesian survival/reliability modeling, Bayesian life-data analysis, Bayesian failure-time analysis	Kaplan-Meier analysis, Cox regression, TTE analysis
Pokrewne	6	3
Podsumowanie	Bayesian reliability analysis estimates how long components or systems survive — their reliability, failure rate, and lifetime distribution — by combining observed (often censored) failure data with prior knowledge through Bayes' rule. As developed in Hamada, Wilson, Reese, and Martz's Bayesian Reliability (2008), it is especially valuable when failures are rare, tests are expensive, and engineering or historical information must be brought to bear.	Survival analysis is a collection of statistical methods for modeling time from a defined starting point until an event of interest occurs (disease, recovery, death, equipment failure). Kaplan and Meier's nonparametric estimator (1958) and David Cox's proportional hazards model (1972) jointly enabled analysis of censored data—individuals whose event times are unknown because they left the study or were still event-free at follow-up. Indispensable in oncology, cardiology, infectious disease research, engineering reliability, and any field where time-to-event matters.