1 citations · 1 across the 4 of their papers we have counts for
5 papers · 1 filter
Multilevel Delayed Acceptance MCMC with an Adaptive Error Model in PyMC3
Mikkel B. Lykkegaard, Grigorios Mingas, Robert Scheichl +2
Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood functions, for instance…
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…
A posteriori stochastic correction of reduced models in delayed acceptance MCMC, with application to multiphase subsurface inverse problems
Tiangang Cui, Colin Fox, Michael J O'Sullivan
Sample-based Bayesian inference provides a route to uncertainty quantification in the geosciences, and inverse problems in general, though is very computationally demanding in the…
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…