94 citations · 364 across the 34 of their papers we have counts for
6 papers · 1 filter
Optimal scaling of MCMC algorithms: the Hamiltonian approach
P. Dobson, J. M. Sanz-Serna, K. C. Zygalakis
We present a simple, yet general approach to study the scaling properties as the dimensionality of Metropolised MCMC sampling algorithms increases. The study relies on the symmetri…
Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling
Teresa Klatzer, Paul Dobson, Yoann Altmann +3
This paper presents a new accelerated proximal Markov chain Monte Carlo methodology to perform Bayesian inference in imaging inverse problems with an underlying convex geometry. Th…
Adaptive multi-stage integration schemes for Hamiltonian Monte Carlo
Lorenzo Nagar, Mario Fernández-Pendás, Jesús María Sanz-Serna +1
Hamiltonian Monte Carlo (HMC) is a powerful tool for Bayesian statistical inference due to its potential to rapidly explore high dimensional state space, avoiding the random walk b…
Split Hamiltonian Monte Carlo revisited
Fernando Casas, Jesús María Sanz-Serna, Luke Shaw
We study Hamiltonian Monte Carlo (HMC) samplers based on splitting the Hamiltonian as , where is quadratic and small. We show that, in general, suc…
Is the NUTS algorithm correct?
J. M. Sanz-Serna
This paper is devoted to investigate whether the popular No U-turn (NUTS) sampling algorithm is correct, i.e.\ whether the target probability distribution is \emph{exactly} conserv…
HMC: avoiding rejections by not using leapfrog and some results on the acceptance rate
M. P. Calvo, D. Sanz-Alonso, J. M. Sanz-Serna
The leapfrog integrator is routinely used within the Hamiltonian Monte Carlo method and its variants. We give strong numerical evidence that alternative, easy to implement algorith…