11 citations · 16 across the 7 of their papers we have counts for
9 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…
Numerical approximation of the Frobenius-Perron operator using the finite volume method
Richard A. Norton, Colin Fox, Malcolm E. Morrison
We develop a finite-dimensional approximation of the Frobenius-Perron operator using the finite volume method applied to the continuity equation for the evolution of probability. A…
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…
Metropolis-Hastings algorithms with autoregressive proposals, and a few examples
Richard A. Norton, Colin Fox
We analyse computational efficiency of Metropolis-Hastings algorithms with stochastic AR(1) process proposals. These proposals include, as a subclass, discretized Langevin diffusio…
Fast sampling in a linear-Gaussian inverse problem
Colin Fox, Richard A. Norton
We solve the inverse problem of deblurring a pixelized image of Jupiter using regularized deconvolution and by sample-based Bayesian inference. By efficiently sampling the marginal…
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…
Regularity for Maxwell eigenproblems in photonic crystal fibre modelling
Monique Dauge, Richard A. Norton, Robert Scheichl
The convergence behaviour and the design of numerical methods for modelling the flow of light in photonic crystal fibres depend critically on an understanding of the regularity of…
Projection methods and discrete gradient methods for preserving first integrals of ODEs
R. A. Norton, D. I. McLaren, G. R. W. Quispel +2
In this paper we study linear projection methods for approximating the solution and simultaneously preserving first integrals of autonomous ordinary differential equations. We show…
Discrete gradient methods for preserving a first integral of an ordinary differential equation
Richard A. Norton, G. R. W. Quispel
In this paper we consider discrete gradient methods for approximating the solution and preserving a first integral (also called a constant of motion) of autonomous ordinary differe…