방법 비교

	강건 베이즈 추론	Markov Chain Monte Carlo (MCMC)
분야	베이지안	시뮬레이션
계열	Bayesian methods	Process / pipeline
기원 연도	1984–1990	1953 (Metropolis-Hastings); 1984 (Gibbs)
창시자	James O. Berger	Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984)
유형	Bayesian sensitivity / robustness framework	Simulation-based Bayesian inference / numerical integration
원전	Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328.	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 sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes	MCMC, Metropolis-Hastings, Gibbs sampling, Markov Zinciri Monte Carlo (MCMC — Metropolis-Hastings, Gibbs)
관련	6	5
요약	Robust Bayesian inference extends standard Bayesian analysis by replacing a single prior distribution with a class of plausible priors and examining how much the posterior conclusions change across that class. Instead of committing to one prior, the analyst bounds the posterior quantity of interest, revealing whether findings are stable or critically dependent on prior assumptions.	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.