58 citations · 65 across the 5 of their papers we have counts for
6 papers
Modelling and analysis of rank ordered data with ties via a generalized Plackett-Luce model
Daniel A. Henderson
A simple generative model for rank ordered data with ties is presented. The model is based on ordering geometric latent variables and can be seen as the discrete counterpart of the…
Multilevel Emulation for Stochastic Computer Models with Application to Large Offshore Wind farms
Jack C. Kennedy, Daniel A. Henderson, Kevin J. Wilson
Renewable energy projects, such as large offshore wind farms, are critical to achieving low-emission targets set by governments. Stochastic computer models allow us to explore futu…
On Bayesian inference for the Extended Plackett-Luce model
Stephen R. Johnson, Daniel A. Henderson, Richard J. Boys
The analysis of rank ordered data has a long history in the statistical literature across a diverse range of applications. In this paper we consider the Extended Plackett-Luce mode…
Emulation of utility functions over a set of permutations: sequencing reliability growth tasks
Kevin J Wilson, Daniel A Henderson, John Quigley
We consider Bayesian design of experiments problems in which we maximise the prior expectation of a utility function over a set of permutations, for example when sequencing a numbe…
Adaptive, delayed-acceptance MCMC for targets with expensive likelihoods
Chris Sherlock, Andrew Golightly, Daniel A. Henderson
When conducting Bayesian inference, delayed acceptance (DA) Metropolis-Hastings (MH) algorithms and DA pseudo-marginal MH algorithms can be applied when it is computationally expen…
Delayed acceptance particle MCMC for exact inference in stochastic kinetic models
Andrew Golightly, Daniel A. Henderson, Chris Sherlock
Recently-proposed particle MCMC methods provide a flexible way of performing Bayesian inference for parameters governing stochastic kinetic models defined as Markov (jump) processe…