This one-hour tutorial introduces new users to version 5 of PyMC, a powerful Python, open source library for probabilistic programming and Bayesian statistical modeling. Participants will learn the fundamentals of PyMC, best practices for installation and setup, and gain hands-on experience building their first Bayesian model.

Join the PyMC development team for a fun and engaging hackathon!

Time series data is ubiquitous, from stock market prices and weather patterns to disease outbreaks and sports outcomes. Accurately modeling these data and generating useful predictions requires specialized techniques due to the unique characteristics of time series data. This tutorial provides a practical introduction to Bayesian time series analysis using PyMC, a powerful probabilistic programming library in Python. Participants will learn how to build, evaluate, and interpret various Bayesian time series models, including ARIMA models, dynamic linear models, and stochastic volatility models. We'll emphasize practical application, covering data preprocessing, model selection, diagnostics, and forecasting, empowering attendees to tackle real-world time series problems with confidence.