Title: Cosmological Forensics: Reading the Geometry of Parameter Inference
What features of a cosmological model determine the shape of its posterior, and how does that shape change when the data depart from our expectations? I will use derivative-based approximations to explore the local geometry of parameter inference, from familiar Fisher forecasts to higher-order descriptions of non-Gaussian structure. I will also discuss computational tools for constructing these approximations and emerging applications that use posterior geometry to diagnose the behavior of an analysis.
For Zoom information, please contact the seminar coordinators.
