## Upcoming Events
Join our Meetup group for more events!
[ Ссылка ]

Austin Rochford: Introduction to Probabilistic Programming with PyMC

## Key Links
- GitHub repo: [ Ссылка ]

## Resources
- Jupyter Notebook: [ Ссылка ]
- Austin's website: [ Ссылка ]
- ArviZ: [ Ссылка ]
- PyMC Series of events: [ Ссылка ]

## 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
00:00:00 Reshama introduces Data Umbrella
00:04:40 Austin begins talk
00:06:15 Talk agenda
00:08:08 Probabilistic programming from two perspectives
00:08:53 What is probabilistic programming?
00:10:15 Mathematical: Monte Carlo Methods
00:13:55 Monty Hall Problem (game: Let's Make a Deal)
00:16:15 Solve Monty Hall Problem using PyMC (solution)
00:18:42 Using Aesara
00:21:00 Doing inference with sampling
00:24:00 What is Aesara? (It is based on Theano.) PyMC's tensor computational backend, fills niche such as PyTorch or TensorFlow.
00:25:20 Using PyMC to do robust regression: with example Anscombe's Quartet
00:28:10 Using ArviZ (library with pre-built visualizations and statistical routines that will help you understand the results of your inference with PyMC.
00:33:08 What is Ridge Regression? (normal priors on your coefficients)
00:36:05 Student-T Distribution
00:39:00 Why are we using Aesara? To do Hamiltonian Monte Carlo.
00:43:10 Bayesian Analysis of Lego Prices
00:49:00 Recommended books
00:50:37 Meenal talks about upcoming PyMC sprint
00:56:30 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: [ Ссылка ]
Twitter: [ Ссылка ]
GitHub: [ Ссылка ]

свернуть