Bayesian methodsBayesian / computational

Dynamic Hamiltonian Monte Carlo

Dynamic Hamiltonian Monte Carlo — widely known as the No-U-Turn Sampler (NUTS) — is an adaptive extension of Hamiltonian Monte Carlo that automatically selects the number of leapfrog integration steps during each MCMC transition, removing the need to hand-tune the most sensitive tuning parameter of standard HMC. It is the default sampler in Stan and PyMC and is suitable for continuous, differentiable posterior distributions of moderate to high dimension.

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Sources

  1. Hoffman, M. D. & Gelman, A. (2014). The No-U-Turn Sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1), 1593–1623. link
  2. Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. Jones & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 113–162). CRC Press. ISBN: 978-1420079418

Related methods

ScholarGateDynamic Hamiltonian Monte Carlo (Dynamic Hamiltonian Monte Carlo (No-U-Turn Sampler)). Retrieved 2026-06-04 from https://scholargate.app/en/bayesian/dynamic-hamiltonian-monte-carlo