방법 비교

	Bayesian Reliability Analysis	Markov Chain Monte Carlo (MCMC)
분야	베이지안	시뮬레이션
계열	Bayesian methods	Process / pipeline
기원 연도	2008	1953 (Metropolis-Hastings); 1984 (Gibbs)
창시자	Bayesian reliability formalized by Hamada, Wilson, Reese & Martz	Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984)
유형	Bayesian model for time-to-failure / reliability data	Simulation-based Bayesian inference / numerical integration
원전	Hamada, M. S., Wilson, A. G., Reese, C. S., & Martz, H. F. (2008). Bayesian Reliability. Springer Series in Statistics. Springer, New York.	Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. & Rubin, D.B. (2013). Bayesian Data Analysis (3rd ed.). Chapman & Hall/CRC.
별칭	Bayesian reliability, Bayesian survival/reliability modeling, Bayesian life-data analysis, Bayesian failure-time analysis	MCMC, Metropolis-Hastings, Gibbs sampling, Markov Zinciri Monte Carlo (MCMC — Metropolis-Hastings, Gibbs)
관련	6	5
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.	Markov Chain Monte Carlo (MCMC) is a family of simulation algorithms that constructs a Markov chain whose stationary distribution is the target posterior, enabling Bayesian inference and high-dimensional integral computation that would otherwise be analytically intractable. Pioneered by Metropolis and colleagues in 1953 and extended by Hastings in 1970, MCMC underpins modern Bayesian statistics. The two most widely used variants are Metropolis-Hastings, which proposes moves from a general proposal distribution, and Gibbs sampling, which draws each parameter in turn from its full conditional distribution.