Compară metode

	Hamiltonian Monte Carlo Robust	Inferența variațională
Domeniu	Bayesian	Bayesian
Familie	Bayesian methods	Bayesian methods
Anul apariției	2010s–2020s	1999
Autorul original	Livingstone, Zanella and related researchers building on Duane et al. (1987)	Jordan, Ghahramani, Jaakkola & Saul
Tip	Robust MCMC sampler	Approximate Bayesian inference
Sursa seminală	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.
Denumiri alternative	Robust HMC, heavy-tailed HMC, geometric-ergodic HMC, outlier-robust HMC	VI, variational Bayes, VB, mean-field variational inference
Înrudite	4	4
Rezumat	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.