We consider sensitivity analysis of Bayesian inverse problems with respect to modeling uncertainties. To this end, we consider sensitivity analysis of the information gain, as measured by the Kullback-Leibler divergence from the posterior to the prior. This choice provides a principled approach that leverages key structures within the Bayesian inverse problem. Also, the information gain…

Extreme events are events that have an extremely low probability of occurring, but often have immense consequences. For this presentation, we focus on extreme events in climate change and wildfires. In the context of climate change, we examine the avoidance of so-called climate tipping points, which are climate regimes where small changes significantly alter the…

Zombies are popularly presented in media as resulting from an infectious outbreak. Various modeling assumptions for zombification as an infectious disease are discussed. Populations during the outbreak are then modeled with infectious disease compartment models accounting for various effects such as latent infectious populations, quarantining, etc. Equilibrium and stability conditions for these dynamics are determined,…