4 papers
On Accelerated Mixing of the No-U-turn Sampler
Stefan Oberdörster
Recent progress on the theory of variational hypocoercivity established that Randomized Hamiltonian Monte Carlo -- at criticality -- can achieve pronounced acceleration in its conv…
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…
Ballistic Convergence in Hit-and-Run Monte Carlo and a Coordinate-free Randomized Kaczmarz Algorithm
Nawaf Bou-Rabee, Andreas Eberle, Stefan Oberdörster
Hit-and-Run is a coordinate-free Gibbs sampler, yet the quantitative advantages of its coordinate-free property remain largely unexplored beyond empirical studies. In this paper, w…
Mixing of the No-U-Turn Sampler and the Geometry of Gaussian Concentration
Nawaf Bou-Rabee, Stefan Oberdörster
We prove that the mixing time of the No-U-Turn Sampler (NUTS), when initialized in the concentration region of the canonical Gaussian measure, scales as , up to logarithmi…