방법 비교

	강건한 해밀토니안 몬테카를로	변분 추론
분야	베이지안	베이지안
계열	Bayesian methods	Bayesian methods
기원 연도	2010s–2020s	1999
창시자	Livingstone, Zanella and related researchers building on Duane et al. (1987)	Jordan, Ghahramani, Jaakkola & Saul
유형	Robust MCMC sampler	Approximate Bayesian inference
원전	Livingstone, S. & Zanella, G. (2022). The Barker proposal: combining robustness and efficiency in gradient-based MCMC. Journal of the Royal Statistical Society: Series B, 84(2), 496–523.	Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183–233.
별칭	Robust HMC, heavy-tailed HMC, geometric-ergodic HMC, outlier-robust HMC	VI, variational Bayes, VB, mean-field variational inference
관련	4	4
요약	Robust Hamiltonian Monte Carlo (Robust HMC) is a family of extensions to standard HMC designed to maintain geometric ergodicity and sampling efficiency when the posterior has heavy tails, strong curvature variation, or near-degenerate geometry. By modifying the kinetic energy, mass matrix, or proposal mechanism, these methods ensure reliable exploration of difficult posteriors that defeat the standard NUTS/HMC sampler.	Variational inference (VI) is a family of techniques that turn Bayesian posterior computation into an optimisation problem. Instead of drawing samples from the exact posterior — as Markov chain Monte Carlo does — VI posits a simpler, tractable family of distributions and finds the member of that family closest to the true posterior by maximising the evidence lower bound (ELBO). Introduced in its modern graphical-model form by Jordan, Ghahramani, Jaakkola and Saul (1999) and given a comprehensive statistical treatment by Blei, Kucukelbir and McAuliffe (2017), VI is now the standard scalable inference engine in probabilistic machine learning.