4 papers
Randomized Reduced Forward Models for Efficient Metropolis--Hastings MCMC, with Application to Subsurface Fluid Flow and Capacitance Tomography
Colin Fox, Tiangang Cui, Markus Neumayer
Bayesian modelling and computational inference by Markov chain Monte Carlo (MCMC) is a principled framework for large-scale uncertainty quantification, though is limited in practic…
Bayesian inference of species trees using diffusion models
Marnus Stoltz, Boris Bauemer, Remco Bouckaert +3
We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried…
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…
Accelerated Gibbs sampling of normal distributions using matrix splittings and polynomials
Colin Fox, Albert Parker
Standard Gibbs sampling applied to a multivariate normal distribution with a specified precision matrix is equivalent in fundamental ways to the Gauss-Seidel iterative solution of…