A major trend in astronomy and healthcare is the rapid adoption of Bayesian statistics for data analysis and modeling. With modern data-sets growing by orders of magnitude in size, the focus is now on developing methods capable of applying contemporary inference techniques to extremely large datasets. To this aim, I present PyAutoFit (https://github.com/rhayes777/PyAutoFit), an open-source probabilistic programming language for automated Bayesian inference.

I will present PyAutoFit’s multi-level modeling framework, which allows a user to compose and fit hierarchical models to extremely large datasets. In an Astronomy setting, I will show how a multi-level model can constrain the dark matter particle, by modeling individual images of strong lens galaxies at the lowest level and the Universe’s cosmological parameters at the top. Next, I will describe a multi-level model of cancer treatment we are building with the healthcare company Roche, where the inner components are how specific genetic or epigenetic profiles of cancers respond to treatments and the higher levels represent tumour dynamics and patient outcomes. Finally, I will discuss how this framework can overcome a major challenge for both Astronomy and healthcare datasets: missing data.