most citedEfficiency and computability of MCMC with Langevin, Hamiltonian, and other matrix-splitting proposals

2 citations · 2 across the 3 of their papers we have counts for

collaborators

3 papers

stat.CO2016

Sampling hyperparameters in hierarchical models: improving on Gibbs for high-dimensional latent fields and large data sets

Richard A. Norton, J. Andres Christen, Colin Fox

We consider posterior sampling in the very common Bayesian hierarchical model in which observed data depends on high-dimensional latent variables that, in turn, depend on relativel…

stat.CO2016

Tuning of MCMC with Langevin, Hamiltonian, and other stochastic autoregressive proposals

Richard A. Norton, Colin Fox

Proposals for Metropolis-Hastings MCMC derived by discretizing Langevin diffusion or Hamiltonian dynamics are examples of stochastic autoregressive proposals that form a natural wi…

math.PR2015★ 2 cited

Efficiency and computability of MCMC with Langevin, Hamiltonian, and other matrix-splitting proposals

Richard A. Norton, Colin Fox

We analyse computational efficiency of Metropolis-Hastings algorithms with AR(1) process proposals. These proposals include, as a subclass, discretized Langevin diffusion (e.g. MAL…