Example: Hamiltonian Monte Carlo
Click on an algorithm below to view an interactive demo where you can change algorithm parameters on-the-fly:
 H. Haario, E. Saksman, and J. Tamminen, An adaptive Metropolis algorithm (2001)
 M. D. Hoffman, A. Gelman, The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo (2011)
 G. O. Roberts, R. L. Tweedie, Exponential Convergence of Langevin Distributions and Their Discrete Approximations (1996)
 Li, Tzu-Mao, et al. Anisotropic Gaussian mutations for metropolis light transport through Hessian-Hamiltonian dynamics ACM Transactions on Graphics 34.6 (2015): 209.
 Q. Liu, et al. Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Advances in Neural Information Processing Systems. 2016.
 J. Buchner A statistical test for Nested Sampling algorithms Statistics and Computing. 2014.
Clone or download the repository and open
index.html in your web browser. All dependencies are included in in
algorithmsdirectory (a good starting point is
Visualizer.prototype.dequeuefunction defined in
main/Visualizer.js. The MCMC simulation adds visualization "events" onto an animation queue. Most common events such as accepting or rejecting a proposal have already been implemented. The renderer composites the contents of three offscreen canvases (densityCanvas, samplesCanvas, and overlayCanvas)
colsproperties and adds many useful linear algebra methods to the object prototype.