Hamiltonian Monte Carlo Without Detailed Balance


Jascha Sohl-Dickstein, Mayur Mudigonda, Michael DeWeese ;
Proceedings of the 31st International Conference on Machine Learning, PMLR 32(1):719-726, 2014.


We present a method for performing Hamiltonian Monte Carlo that largely eliminates sample rejection. In situations that would normally lead to rejection, instead a longer trajectory is computed until a new state is reached that can be accepted. This is achieved using Markov chain transitions that satisfy the fixed point equation, but do not satisfy detailed balance. The resulting algorithm significantly suppresses the random walk behavior and wasted function evaluations that are typically the consequence of update rejection. We demonstrate a greater than factor of two improvement in mixing time on three test problems. We release the source code as Python and MATLAB packages.

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