  We present a selection criterion for the Euclidean metric adapted during
warmup in a Hamiltonian Monte Carlo sampler that makes it possible for a
sampler to automatically pick the metric based on the model and the
availability of warmup draws. Additionally, we present a new adaptation
inspired by the selection criterion that requires significantly fewer warmup
draws to be effective. The effectiveness of the selection criterion and
adaptation are demonstrated on a number of applied problems. An implementation
for the Stan probabilistic programming language is provided.
