5 papers
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…
Numerical Generalized Randomized Hamiltonian Monte Carlo for piecewise smooth target densities
Jimmy Huy Tran, Tore Selland Kleppe
Traditional gradient-based sampling methods, like standard Hamiltonian Monte Carlo, require that the desired target distribution is continuous and differentiable. This limits the t…
Randomized Runge-Kutta-Nyström Methods for Unadjusted Hamiltonian and Kinetic Langevin Monte Carlo
Nawaf Bou-Rabee, Tore Selland Kleppe
We introduce - and -order -accurate randomized Runge-Kutta-Nyström methods, tailored for approximating Hamiltonian flows within non-reversible Markov chain Monte Ca…
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…
Numerical Generalized Randomized HMC processes for restricted domains
Tore Selland Kleppe, Roman Liesenfeld
We propose a generic approach for numerically efficient simulation from analytically intractable distributions with constrained support. Our approach relies upon Generalized Random…