The goal of this talk is to introduce the Hamiltonian Monte Carlo method -- a physics-inspired algorithm for sampling from Gibbs densities. We focus on the ``idealized'' case, where one can compute continuous trajectories exactly. We show that idealized HMC preserves the target density and we establish its convergence when $f$ is strongly convex.

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