This tutorial walks through how one can use MPoL with the probabilistic programming language Pyro to do Bayesian inference with visibility datasets. We show how to use Stochastic Variational Inference (SVI), but Hamiltonian Monte Carlo samplers are also possible.
The "source" of the tutorial is the pyro.md file, and the output .ipynb file is generated via
jupytext --to ipynb --execute pyro.md
However, if you're just following along, you can view the pyro.ipynb file directly, since it contains the figure outputs.