This repository serves as an educational resource for Bayesian statistical modeling, providing a collection of instructional examples that translate theoretical concepts into executable Python code. It functions as a computational framework for performing statistical inference and parameter estimation, designed to help users learn and apply probabilistic programming techniques through interactive documentation.
The project utilizes a probabilistic programming framework to define statistical models as directed acyclic graphs, enabling automated inference through advanced sampling algorithms. By leveraging Hamiltonian Monte Carlo sampling and automatic differentiation, the models explore high-dimensional probability distributions to generate posterior samples. The implementation relies on vectorized array computation to handle complex mathematical operations across datasets simultaneously.
The collection covers a broad range of scientific data analysis tasks, including the construction of Bayesian hierarchical models that allow for information sharing across groups. These examples are organized within a computational notebook environment, which interleaves narrative text with code to document the iterative process of building, testing, and validating statistical hypotheses.
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
