On the application of improved symplectic integrators in Hamiltonian Monte Carlo. Issue 2 (7th February 2018) - Mannseth, Janne; Kleppe, Tore S.; Skaug, Hans J.
ABSTRACT: We explore the construction of new symplectic numerical integration schemes to be used in Hamiltonian Monte Carlo and study their efficiency. Integration schemes from Blanes et al., and a new scheme are considered as candidates to the commonly used leapfrog method. All integration schemes are tested within the framework of the No-U-Turn sampler (NUTS), both for a logistic regression model and a student t -model. The results show that the leapfrog method is inferior to all the new methods both in terms of asymptotic expected acceptance probability for a model problem and the efficient sample size per computing time for the realistic models.