  The goal of this article is to introduce the Hamiltonian Monte Carlo (HMC)
method -- a Hamiltonian dynamics-inspired algorithm for sampling from a Gibbs
density $\pi(x) \propto e^{-f(x)}$. We focus on the "idealized" case, where one
can compute continuous trajectories exactly. We show that idealized HMC
preserves $\pi$ and we establish its convergence when $f$ is strongly convex
and smooth.
