Electron Multiplying Charge Coupled Device (EMCCD) cameras are widely used for fluorescence microscopy experiments. However, the quantitative determination of biological parameters uniquely depends on characteristics of the unavoidably inhomogenous illumination profile as it gives rise to an image. It is therefore of interest to learn this inhomogenous illumination profiles that can dramatically vary across images alongside the camera parameters though a detailed camera model. In this manuscript we create a detailed model to learn inhomogeneous illumination profiles as well as learn all associated camera parameters. We achieve this within a Bayesian paradigm allowing us to determine full distributions over the unknowns.