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stat.CO2025
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…
stat.CO2025
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…