is ineﬃcient at exploring complex posteriors • It doesn’t scale to more than ~20 dimensions (parameters) • Often fails silently Metropolis Hastings Credit: https://github.com/chi-feng/mcmc-demo Posterior
exoplanets and objects such as Brown Dwarfs and Black Holes • Fitting models is hard because the physics of interest maps poorly onto the observed data • Correlated noise matters • Hamiltonian Monte Carlo eﬃciently samples posteriors using information about the geometry of the posterior probability density