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The author shares notes explaining Hamiltonian Monte Carlo (HMC) from a probabilistic perspective to enhance understanding without relying on physics-based motivation.
This paper introduces Neural Non-Equilibrium Hamiltonian Monte Carlo (NHMC), a train-then-correct method for sampling from unnormalized Boltzmann densities by learning stochastic Hamiltonian-style paths and correcting them using non-equilibrium work.