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stat.CO2024
On the convergence of dynamic implementations of Hamiltonian Monte Carlo and No U-Turn Samplers
Alain Durmus, Samuel Gruffaz, Miika Kailas +2
There is substantial empirical evidence about the success of dynamic implementations of Hamiltonian Monte Carlo (HMC), such as the No U-Turn Sampler (NUTS), in many challenging inf…
stat.CO2024
Optimal Scaling Results for Moreau-Yosida Metropolis-adjusted Langevin Algorithms
Francesca R. Crucinio, Alain Durmus, Pablo Jiménez +1
We consider a recently proposed class of MCMC methods which uses proximity maps instead of gradients to build proposal mechanisms which can be employed for both differentiable and…