The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Abstract
Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior and sensitivity to correlated parameters that plague many MCMC methods by taking a series of steps informed by first-order gradient information. These features allow it to converge to high-dimensional target distributions much more quickly than simpler methods such as random walk Metropolis or Gibbs sampling. However, HMC's...
Paper Details
Title
The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo
Published Date
Jan 1, 2014
Journal
Volume
15
Issue
1
Pages
1593 - 1623
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