Hamiltonian Monte Carlo (HMC) is a statistical sampling method that uses principles from Hamiltonian mechanics to explore probability distributions efficiently. By treating the parameters of interest as physical particles, HMC utilizes gradients of the target distribution to guide the sampling process, resulting in faster convergence and more accurate estimates in parameter estimation and inverse problems.
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