NoUTurnSampler
The No-U-Turn Sampler (NUTS) is a sampling algorithm for Bayesian inference that extends Hamiltonian Monte Carlo (HMC) by adaptively choosing the length of the simulated Hamiltonian trajectory. It removes the need to pre-specify how many leapfrog steps to take, instead terminating when the trajectory would begin to turn back toward its starting point, a No-U-Turn condition that improves efficiency and robustness.
During each iteration, NUTS draws a fresh momentum from a Gaussian distribution, integrates the Hamiltonian dynamics
History and use: The No-U-Turn Sampler was introduced to provide automatic path-length tuning for HMC. It has
Advantages and limitations: NUTS offers improved efficiency and reduces the manual tuning burden compared with fixed-length