Speaker
Pablo LEMOS
(University of Sussex)
Description
Bayesian parameter estimation and model comparison are widely used in cosmology. This has led to the development of very efficient and user-friendly codes that perform these complex calculations. In this presentation, we will demonstrate the wider applicability of these algorithms, by applying them to study the COVID pandemic. We will perform Bayesian parameter estimation and model comparison using MCMC and Nested Sampling on different variations of the SIR model. This serves not only to learn which models of the pandemic are favored by the data but also to illustrate the usefulness of these algorithms outside of cosmology.
Primary author
Pablo LEMOS
(University of Sussex)
Co-authors
Prof.
Lahav OFER
(University College London)
Mrs
Nicolaou CONSTANTINA
(University College London)
Mr
Henghes BEN
(University College London)
