Speaker
Description
A major trend in Physics and 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.
In this hands on demonstration, I will:
1) Give an overview of how to compose a probabilistic model and perform automated Bayesian inference.
2) Demonstrate a simple model-fitting example using a Cosmology based science-case.
3) Illustrate the use of Bayesian graphs to perform simultaneous inference of thousands on datasets.
