DBDA-python provides Python code implementing models and figures from 'Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan' (2nd ed.) by Kruschke (2015). This repository focuses on using the PyMC3 probabilistic programming library to perform Bayesian data analysis. The code offers a hands-on approach to applying Bayesian methods to various statistical problems, enabling users to explore and understand complex models.
This repository provides a comprehensive collection of Jupyter Notebooks, each focusing on a specific chapter from the textbook. The code is well-structured and leverages the power of PyMC3 for model fitting and inference. It includes examples for various types of predictor variables and model comparison techniques, offering a practical guide to Bayesian data analysis.
- Bayesian Modeling: Implements various Bayesian models as described in the textbook.
- PyMC3 Integration: Utilizes PyMC3 for probabilistic programming and model inference.
- Diverse Predictor Types: Includes examples for metric, nominal, ordinal, and dichotomous predictor variables.
- Model Comparison: Offers code for comparing different models using Bayesian approaches.
- Chapter-Based Structure: Notebooks correspond to chapters in the textbook for easy navigation.
- Clear Explanations: Notebooks integrate code execution with detailed explanations and visualization.
- Data Analysis Examples: Provides concrete examples of how to perform data analysis using Bayesian methods.
The repository is well-established with a history of updates and active maintenance. Recent commits indicate continued support and bug fixes. The code is documented within the Jupyter Notebooks, though comprehensive external documentation is minimal. The project has a modest community of users and contributors.
This project benefits researchers, students, and practitioners looking to learn and apply Bayesian data analysis techniques with PyMC3. It's particularly valuable for those following along with the 'Doing Bayesian Data Analysis' textbook, offering practical code examples and implementation details. The repository provides a direct implementation of the book concepts, simplifying the transition from theoretical understanding to real-world application.