Evaluation of NUTS — more comments on the paper by Hoffman and Gelman

Here is my second post on the paper by Matthew Hoffman and Andrew Gelman on “The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo”, available from arxiv.org. In my first post, I discussed how well the two main innovations in this “NUTS’” method — ending a trajectory when a “U-Turn” is encountered, and adaptively setting the stepsize — can be expected to work. In this post, I will discuss the empirical evaluations in the NUTS paper, and report on an evaluation of my own, made possible by the authors having kindly made available the NUTS software, concluding that the paper’s claims for NUTS are somewhat overstated. The issues I discuss are also of more general interest for other evaluations of HMC.