Hamiltonian Monte Carlo (HMC) is a sophisticated sampling method used in Bayesian inference that leverages concepts from physics, specifically Hamiltonian dynamics, to efficiently explore the posterior distribution of parameters. By simulating the movement of particles in a potential energy landscape, HMC can generate samples that are correlated and more representative of the target distribution. This method enhances the efficiency of Markov Chain Monte Carlo (MCMC) techniques by minimizing random walk behavior and improving convergence.
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