33 citations · 54 across the 17 of their papers we have counts for
19 papers
Posterior exploration for computationally intensive forward models
Mikkel B. Lykkegaard, Colin Fox, Dave Higdon +2
In this chapter, we address the challenge of exploring the posterior distributions of Bayesian inverse problems with computationally intensive forward models. We consider various m…
Multilevel Delayed Acceptance MCMC
Mikkel B. Lykkegaard, Tim J. Dodwell, Colin Fox +2
We develop a novel Markov chain Monte Carlo (MCMC) method that exploits a hierarchy of models of increasing complexity to efficiently generate samples from an unnormalized target d…
Parsimony and the rank of a flattening matrix
Jandre Snyman, Colin Fox, David Bryant
The standard models of sequence evolution on a tree determine probabilities for every character or site pattern. A flattening is an arrangement of these probabilities into a matrix…
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…
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…