20 citations · 71 across the 15 of their papers we have counts for
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Faster parallel MCMC: Metropolis adjustment is best served warm
Jakob Robnik, Uroš Seljak
Despite the enormous success of Hamiltonian Monte Carlo and related Markov Chain Monte Carlo (MCMC) methods, sampling often still represents the computational bottleneck in scienti…
Metropolis Adjusted Microcanonical Hamiltonian Monte Carlo
Jakob Robnik, Reuben Cohn-Gordon, Uroš Seljak
Sampling from high dimensional distributions is a computational bottleneck in many scientific applications. Hamiltonian Monte Carlo (HMC), and in particular the No-U-Turn Sampler (…
Practical and Scalable Hamiltonian Monte Carlo Without the Metropolis Test
Jakob Robnik, Reuben Cohn-Gordon, Uroš Seljak
Hamiltonian Monte Carlo and underdamped Langevin Monte Carlo are leading methods for sampling from high-dimensional distributions with differentiable densities. Both rely on numeri…
Microcanonical Hamiltonian Monte Carlo
Jakob Robnik, G. Bruno De Luca, Eva Silverstein +1
We develop Microcanonical Hamiltonian Monte Carlo (MCHMC), a class of models which follow a fixed energy Hamiltonian dynamics, in contrast to Hamiltonian Monte Carlo (HMC), which f…