방법 비교

	Bayesian Reliability Analysis	Kaplan-Meier 생존 추정량
분야	베이지안	생존분석
계열	Bayesian methods	Survival analysis
기원 연도	2008	1958
창시자	Bayesian reliability formalized by Hamada, Wilson, Reese & Martz	Kaplan, E. L. & Meier, P.
유형	Bayesian model for time-to-failure / reliability data	Non-parametric survival estimator
원전	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.
별칭	Bayesian reliability, Bayesian survival/reliability modeling, Bayesian life-data analysis, Bayesian failure-time analysis	product-limit estimator, km curve, kaplan-meier sağkalım analizi
관련	6	2
요약	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.	The Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.