An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo

Matthew Hoffman · Alexey Radul · Pavel Sountsov

Keywords: [ Applications ] [ Algorithms ] [ Probabilistic Methods ] [ Unsupervised Learning ] [ Network Analysis ] [ Sampling ]

[ Abstract ]
Thu 15 Apr 7:30 a.m. PDT — 9:30 a.m. PDT
Oral presentation: Sampling Methods
Tue 13 Apr 11:30 a.m. PDT — 12:30 p.m. PDT


Hamiltonian Monte Carlo (HMC) is a powerful MCMC algorithm based on simulating Hamiltonian dynamics. Its performance depends strongly on choosing appropriate values for two parameters: the step size used in the simulation, and how long the simulation runs for. The step-size parameter can be tuned using standard adaptive-MCMC strategies, but it is less obvious how to tune the simulation-length parameter. The no-U-turn sampler (NUTS) eliminates this problematic simulation-length parameter, but NUTS’s relatively complex control flow makes it difficult to efficiently run many parallel chains on accelerators such as GPUs. NUTS also spends some extra gradient evaluations relative to HMC in order to decide how long to run each iteration without violating detailed balance. We propose ChEES-HMC, a simple adaptive-MCMC scheme for automatically tuning HMC’s simulation-length parameter, which minimizes a proxy for the autocorrelation of the state’s second moments. We evaluate ChEES-HMC and NUTS on many tasks, and find that ChEES-HMC typically yields larger effective sample sizes per gradient evaluation than NUTS does. When running many chains on a GPU, ChEES-HMC can also run significantly more gradient evaluations per second than NUTS, allowing it to quickly provide accurate estimates of posterior expectations.

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