Allen Downey
Allen Downey is a principal data scientist at PyMC Labs and professor emeritus at Olin College. He is the author of several books including Think Python, Think Bayes, and Probably Overthinking It -- and a blog about programming and data science. He received a Ph.D. in computer science from the University of California, Berkeley, and Bachelor's and Masters degrees from MIT.
Session
This hands-on tutorial introduces practical Bayesian inference using PyMC, focusing on A/B testing, decision-making under uncertainty, and hierarchical modeling. With real-world examples, you'll learn how to build and interpret Bayesian models, evaluate competing hypotheses, and implement adaptive strategies like Thompson sampling. Whether you're working in marketing, healthcare, public policy, UX design, or data science more broadly, these techniques offer powerful tools for experimentation, decision-making, and evidence-based analysis.