SurveyDataPyMC

Abstract

In this tutorial, we explore Bayesian regression using PyMC – the primary library for Bayesian sampling in Python – focusing on survey data and other datasets with categorical outcomes. Starting with logistic regression, we’ll build up to categorical and ordered logistic regression, showcasing how Bayesian approaches provide versatile tools for developing and evaluating complex models. Participants will leave with practical skills for implementing Bayesian regression models in PyMC, along with a deeper appreciation for the power of Bayesian inference in real-world data analysis. Participants should be familiar with Python, the SciPy ecosystem, and basic statistics, but no experience with Bayesian methods is required.

Description

In this hands-on tutorial, we will dive into the world of Bayesian regression using PyMC, with a particular focus on datasets that feature categorical outcomes. PyMC provides versatile tools for iterative development of powerful models. This tutorial guides participants through the fundamentals of Bayesian regression, starting from logistic regression and extending to categorical and ordered logistic regression. It also introduces the PyMC package, its syntax and capabilities.

Run the notebooks

Use these links to run the notebooks on Colab:

• Notebook 0: Introduction to Bayesian Sampling

• Notebook 1: Logistic Regression

• Notebook 2: Categorical Regression

• Notebook 3: Ordinal Regression

Note: The notebooks use data from the General Social Survey (GSS). The notebooks will download the data when needed.