In geostatistics and also in other applications in science and engineering, it is now
common to perform updates on Gaussian process models with many thousands or even millions
of components. These large-scale inferences involve modelling, representational and computational
challenges. We describe a visualization tool for large-scale Gaussian updates, the ‘medal plot’.
The medal plot shows the updated uncertainty at each observation location and also summarizes
the sharing of information across observations, as a proxy for the sharing of information across the
state vector (or latent process). As such, it reflects characteristics of both the observations and the
statistical model.We illustrate with an application to assess mass trends in the Antarctic Ice Sheet,
for which there are strong constraints from the observations and the physics.