2 citations · 3 across the 5 of their papers we have counts for
5 papers
Preconditioning Hamiltonian Monte Carlo by minimizing Fisher Divergence
Adrian Seyboldt, Eliot L. Carlson, Bob Carpenter
Although Hamiltonian Monte Carlo (HMC) scales as O(d^(1/4)) in dimension, there is a large constant factor determined by the curvature of the target density. This constant factor c…
The Within-Orbit Adaptive Leapfrog No-U-Turn Sampler
Nawaf Bou-Rabee, Bob Carpenter, Tore Selland Kleppe +1
Locally adapting parameters within Markov chain Monte Carlo methods while preserving reversibility is notoriously difficult. The success of the No-U-Turn Sampler (NUTS) largely ste…
The No-Underrun Sampler: A Locally-Adaptive, Gradient-Free MCMC Method
Nawaf Bou-Rabee, Bob Carpenter, Sifan Liu +1
In this work, we introduce the No-Underrun Sampler (NURS), a locally-adaptive, gradient-free Markov chain Monte Carlo method that blends ideas from Hit-and-Run and the No-U-Turn Sa…
Incorporating Local Step-Size Adaptivity into the No-U-Turn Sampler using Gibbs Self Tuning
Nawaf Bou-Rabee, Bob Carpenter, Tore Selland Kleppe +1
Adapting the step size locally in the no-U-turn sampler (NUTS) is challenging because the step-size and path-length tuning parameters are interdependent. The determination of an op…
GIST: Gibbs self-tuning for locally adaptive Hamiltonian Monte Carlo
Nawaf Bou-Rabee, Bob Carpenter, Milo Marsden
We introduce a novel and flexible framework for constructing locally adaptive Hamiltonian Monte Carlo (HMC) samplers by Gibbs sampling the algorithm's tuning parameters conditional…