138 citations · 184 across the 6 of their papers we have counts for
6 papers
The Lifebelt Particle Filter for robust estimation from low-valued count data
Alice Corbella, Trevelyan J. McKinley, Paul J. Birrell +4
Particle filtering methods can be applied to estimation problems in discrete spaces on bounded domains, to sample from and marginalise over unknown hidden states. As in continuous…
Emulation and History Matching using the hmer Package
Andrew Iskauskas, Ian Vernon, Michael Goldstein +4
Modelling complex real-world situations such as infectious diseases, geological phenomena, and biological processes can present a dilemma: the computer model (referred to as a simu…
Efficient Bayesian model selection for coupled hidden Markov models with application to infectious diseases
Jake Carson, Trevelyan J. McKinley, Peter Neal +1
Performing model selection for coupled hidden Markov models (CHMMs) is highly challenging, owing to the large dimension of the hidden state process. Whilst in principle the hidden…
Key Questions for Modelling COVID-19 Exit Strategies
Robin N Thompson, T Deirdre Hollingsworth, Valerie Isham +40
Combinations of intense non-pharmaceutical interventions ('lockdowns') were introduced in countries worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to impl…
Model comparison with missing data using MCMC and importance sampling
Panayiota Touloupou, Naif Alzahrani, Peter Neal +2
Selecting between competing statistical models is a challenging problem especially when the competing models are non-nested. In this paper we offer a simple solution by devising an…
Bayesian Model Choice in Cumulative Link Ordinal Regression Models
Trevelyan J. McKinley, Michelle Morters, James L. N. Wood
The use of the proportional odds (PO) model for ordinal regression is ubiquitous in the literature. If the assumption of parallel lines does not hold for the data, then an alternat…