756 citations · 3.9k across the 84 of their papers we have counts for
91 papers
Bayesian inference for high-dimensional discrete-time epidemic models: spatial dynamics of the UK COVID-19 outbreak
Chris P Jewell, Alison C Hale, Barry S Rowlingson +3
Stochastic epidemic models which incorporate interactions between space and human mobility are a key tool to inform prioritisation of outbreak control to appropriate locations. How…
Scaling of Piecewise Deterministic Monte Carlo for Anisotropic Targets
Joris Bierkens, Kengo Kamatani, Gareth O. Roberts
Piecewise deterministic Markov processes (PDMPs) are a type of continuous-time Markov process that combine deterministic flows with jumps. Recently, PDMPs have garnered attention w…
Methods and applications of PDMP samplers with boundary conditions
Joris Bierkens, Sebastiano Grazzi, Gareth Roberts +1
We extend Monte Carlo samplers based on piecewise deterministic Markov processes (PDMP samplers) by formally defining different boundary conditions such as sticky floors, soft and…
Optimal Scaling Results for Moreau-Yosida Metropolis-adjusted Langevin Algorithms
Francesca R. Crucinio, Alain Durmus, Pablo Jiménez +1
We consider a recently proposed class of MCMC methods which uses proximity maps instead of gradients to build proposal mechanisms which can be employed for both differentiable and…
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…
Sampling using Adaptive Regenerative Processes
Hector McKimm, Andi Q Wang, Murray Pollock +2
Enriching Brownian motion with regenerations from a fixed regeneration distribution at a particular regeneration rate results in a Markov process that has a target distribu…