5 papers
A discomfort-informed adaptive Gibbs sampler for finite mixture models
Davide Fabbrico, Andi Q. Wang, Sebastiano Grazzi +5
Finite mixture models are frequently used to uncover latent structures in high-dimensional datasets (e.g.\ identifying clusters of patients in electronic health records). The infer…
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…
Automatic Zig-Zag sampling in practice
Alice Corbella, Simon E F Spencer, Gareth O Roberts
Novel Monte Carlo methods to generate samples from a target distribution, such as a posterior from a Bayesian analysis, have rapidly expanded in the past decade. Algorithms based o…
Inferring Epidemics from Multiple Dependent Data via Pseudo-Marginal Methods
Alice Corbella, Anne M Presanis, Paul J Birrell +1
Health-policy planning requires evidence on the burden that epidemics place on healthcare systems. Multiple, often dependent, datasets provide a noisy and fragmented signal from th…
Exploiting routinely collected severe case data to monitor and predict influenza outbreaks
Alice Corbella, Xu-Sheng Zhang, Paul J. Birrell +4
Influenza remains a significant burden on health systems. Effective responses rely on the timely understanding of the magnitude and the evolution of an outbreak. For monitoring pur…