# Talks

#### Introduction to Probabilistic Programming with PyMC

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 will give 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.

- Data Umbrella PyMC Sprint • January 11, 2022 • Jupyter Notebook

#### The Hamiltonian Monte Carlo Revolution is Open Source: Probabilistic Programming with PyMC3

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. PyMC3 is one such package written in Python and supported by NumFOCUS. This talk will give an introduction to probabilistic programming with PyMC3, 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.

- Open Data Science Conference East • May 2, 2019 • Slides • Jupyter Notebook
- Tom Tom Founders Festival Applied Machine Learning Conference • April 11, 2019 • Slides • Jupyter Notebook
- Open Data Science Conference West • November 2, 2018 • Slides • Jupyter Notebook
- Open Data Science Conference East • May 3, 2018 • Jupyter Notebook

#### Two Years of Bayesian Bandits for E-Commerce

At Monetate, we've deployed Bayesian bandits (both noncontextual and contextual) to help our clients optimize their e-commerce sites since early 2016. This talk is an overview of the lessons we've learned from both the processes of deploying real-time Bayesian machine learning systems at scale and building a data product on top of these systems that is accessible to non-technical users (marketers). This talk will cover:

- The place of multi-armed bandits in the A/B testing industry,
- Thompson sampling and the basic theory of Bayesian bandits,
- Bayesian approaches for accommodating nonstationarity in bandit feedback,
- User experience challenges in driving adoption of these technologies by nontechnical marketers.

- New York City College of Technology Math Club • April 18, 2019 • Slides • Jupyter Notebook
- PyData NYC 2018 • October 18, 2018 • Slides • Jupyter Notebook
- Tom Tom Founders Festival Applied Machine Learning Conference • April 12, 2018 • Slides • Jupyter Notebook
- Data Philly • April 2, 2018 • Slides • Jupyter Notebook
- Boston Bayesians • January 22, 2018 • Slides • Jupyter Notebook

#### Understanding NBA Foul Calls with Python

Since 2015, the NBA has released a detailed report of foul calls and non-calls that occur in the final two minutes of close games. This talk is a case study in using open source Python packages to analyze these reports in order to understand the relationship between game dynamics, player abilities, and foul calls. Our main goal is to quantify the relationship between player ability and foul calls. Since intentional fouls are a ubiquitous part of the NBA endgame, this data set also contains rich information about the relationship between game dynamics and intentional fouls for us to model.

- PyData NYC • November 27, 2017 • Slides • Jupyter Notebook

#### Open Source Bayesian Inference in Python with PyMC3

In the last ten years, there have been a number of advancements in the study of Hamiltonian Monte Carlo 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 programmers and statisticians. PyMC3 is one such package written in Python and supported by NumFOCUS. This workshop will give an introduction to probabilistic programming with PyMC3. No preexisting knowledge of Bayesian statistics is necessary; a working knowledge of Python will be helpful.

- FOSSCON • August 25, 2017 • Slides • Jupyter Notebook