[1] H. Haario, E. Saksman, and J. Tamminen, An adaptive Metropolis algorithm (2001)

[2] M. D. Hoffman, A. Gelman, The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo (2011)

[3] G. O. Roberts, R. L. Tweedie, Exponential Convergence of Langevin Distributions and Their Discrete Approximations (1996)

[4] Li, Tzu-Mao, et al. Anisotropic Gaussian mutations for metropolis light transport through Hessian-Hamiltonian dynamics ACM Transactions on Graphics 34.6 (2015): 209.

[5] Q. Liu, et al. Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Advances in Neural Information Processing Systems. 2016.

[6] J. Buchner A statistical test for Nested Sampling algorithms Statistics and Computing. 2014.

[7] Cajo J. F. ter Braak & Jasper A. Vrugt Differential Evolution Markov Chain with snooker updater and fewer chains Statistics and Computing. 2008.

[9] M. Welling, Y. W. Teh Bayesian Learning via Stochastic Gradient Langevin Dynamics ICML. 2011.

[10] J. Baker, P. Fearnhead, E. B. Fox, C. Nemeth Control variates for stochastic gradient MCMC Statistics and Computing. 2019.

[11] L. Sharrock, C. Nemeth Tuning-Free Sampling via Optimization on the Space of Probability Measures (2025)

[12] J. Bierkens, P. Fearnhead, G. Roberts The Zig-Zag process and super-efficient sampling for Bayesian analysis of big data Annals of Statistics. 2019.

[13] A. Bouchard-Côté, S. J. Vollmer, A. Doucet The Bouncy Particle Sampler: A Nonreversible Rejection-Free Markov Chain Monte Carlo Method Journal of the American Statistical Association. 2018.