= Surrogate Hamiltonian Monte Carlo
<Surrogate Hamiltonian Monte Carlo> uses an auxiliary smooth <probability density function> $\nu$ to generate trajectories but applies a <Metropolis–Hastings acceptance probability> using the intended target $\mu$. For Gaussian momenta and an <involutive Metropolis proposal> generated by flipped <leapfrog integration>, the acceptance probability is $\min(1,\mu(x')e^{-\|p'\|^2/2}/[\mu(x)e^{-\|p\|^2/2}])$. Only the surrogate <gradient> is required during the trajectory; target density evaluations are still required at the endpoints.
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