Speaker
Description
Splitting schemes provide a promising alternative to the classical
Stormer-Verlet method in Hamiltonian Monte Carlo (HMC) methodology.
Within the family of one-parameter second-order splitting procedures, we
demonstrate that using a designated function of the free parameter to
select the step size ensures stability and Hamiltonian preservation when
sampling from Gaussian distributions. This guarantees no sample
rejections in the HMC process, a key factor for superior performance
compared to recent similar methods. The effectiveness of the proposed
approach for sampling from general non-Gaussian distributions is
assessed, incorporating a simple adaptive selection technique for the
free parameter to improve HMC performance. Benchmark examples from
literature and experiments, including the Log-Gaussian Cox process and
Bayesian Logistic Regression, highlight the effectiveness of the
approach.
