The Markov-chain Monte Carlo Interactive Gallery

Click on an algorithm below to view interactive demo:

View the source code on github:


[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.

[8] Jakob Robnik, G. Bruno De Luca, Eva Silverstein, UroŇ° Seljak Microcanonical Hamiltonian Monte Carlo