[41] Intro to Probabilistic Programming with PyMC (Austin Rochford)

## Upcoming Events Join our Meetup group for more events! https://www.meetup.com/data-umbrella Austin Rochford: Introduction to Probabilistic Programming with PyMC ## Key Links - GitHub repo: https://pymc-data-umbrella.xyz/en/latest/about/probabilistic_programming_with_pymc/notebook.html#probprog-pymc-nb ## Resources - Jupyter Notebook: https://github.com/pymc-devs/pymc-data-umbrella/blob/main/about/probabilistic_programming_with_pymc/notebook.ipynb - Austin's website: https://austinrochford.com/talks.html - ArviZ: https://github.com/arviz-devs/arviz - PyMC Series of events: https://pymc-data-umbrella.xyz ## Topics Covered - Probabilistic programming from two perspectives -- Philosophical: storytelling with data -- Mathematical: Monte Carlo methods - Probabilistic programming with PyMC -- The Monty Hall problem -- Robust regression - Hamiltonian Monte Carlo -- Aesara - Lego example - Next Steps ## Agenda 00:00 Reshama introduces Data Umbrella 04:40 Austin begins talk 06:15 Talk agenda 08:08 Probabilistic programming from two perspectives 08:53 What is probabilistic programming? 10:15 Mathematical: Monte Carlo Methods 13:55 Monty Hall Problem (game: Let's Make a Deal) 16:15 Solve Monty Hall Problem using PyMC (solution) 18:42 Using Aesara 21:00 Doing inference with sampling 24:00 What is Aesara? (It is based on Theano.) PyMC's tensor computational backend, fills niche such as PyTorch or TensorFlow. 25:20 Using PyMC to do robust regression: with example Anscombe's Quartet 28:10 Using ArviZ (library with pre-built visualizations and statistical routines that will help you understand the results of your inference with PyMC. 33:08 What is Ridge Regression? (normal priors on your coefficients) 36:05 Student-T Distribution 39:00 Why are we using Aesara? To do Hamiltonian Monte Carlo. 43:10 Bayesian Analysis of Lego Prices 49:00 Recommended books 50:37 Meenal talks about upcoming PyMC sprint 56:30 Q&A with Austin ## Event In the last ten years, there have been a number of advancements in the study of Hamiltonian Monte Carlo and variational inference algorithms that have enabled effective Bayesian statistical computation for much more complicated models than were previously feasible. These algorithmic advancements have been accompanied by a number of open source probabilistic programming packages that make them accessible to the general engineering, statistics, and data science communities. PyMC is one such package written in Python and supported by NumFOCUS. This talk gives an introduction to probabilistic programming with PyMC, with a particular emphasis on the how open source probabilistic programming makes Bayesian inference algorithms near the frontier of academic research accessible to a wide audience. ## About the Speaker Austin Rochford is the Chief Data Scientist at Kibo Commerce. He is a recovering mathematician and is passionate about math education, Bayesian statistics, and machine learning. LinkedIn: https://www.linkedin.com/in/austin-rochford/ Twitter: https://twitter.com/AustinRochford GitHub: https://github.com/AustinRochford/