Split Hamiltonian Monte Carlo revisited
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We study Hamiltonian Monte Carlo (HMC) samplers based on splitting the Hamiltonian H as H_0(θ,p)+U_1(θ), where H_0 is quadratic and U_1 small. We show that, in general, such samplers suffer from stepsize stability restrictions similar to those of algorithms based on the standard leapfrog integrator. The restrictions may be circumvented by preconditioning the dynamics. Numerical experiments show that, when the H_0(θ,p)+U_1(θ) splitting is combined with preconditioning, it is possible to construct samplers far more efficient than standard leapfrog HMC.
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