33 citations · 35 across the 4 of their papers we have counts for
4 papers
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…
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…
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…
Coupled MCMC with a randomized acceptance probability
Geoff K. Nicholls, Colin Fox, Alexis Muir Watt
We consider Metropolis Hastings MCMC in cases where the log of the ratio of target distributions is replaced by an estimator. The estimator is based on m samples from an independen…