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stat.ME2025
Bayesian Inference of Reproduction Number from Epidemiological and Genetic Data Using Particle MCMC
Alicia Gill, Jere Koskela, Xavier Didelot +1
Inference of the reproduction number through time is of vital importance during an epidemic outbreak. Typically, epidemiologists tackle this using observed prevalence or incidence…
stat.ME2025
Improved MCMC with active subspaces
Leonardo Ripoli, Richard G. Everitt
Constantine et al. (2016) introduced a Metropolis-Hastings (MH) approach that target the active subspace of a posterior distribution: a linearly projected subspace that is informed…
stat.ME2024
Ensemble Kalman inversion approximate Bayesian computation
Richard G Everitt
Approximate Bayesian computation (ABC) is the most popular approach to inferring parameters in the case where the data model is specified in the form of a simulator. It is not poss…