Reproducing "Model-Based Machine Learning" Examples with Pyro

Description

This project is part of my journey to learn Pyro, a universal probabilistic programming language. I'm working through examples from the excellent "Model-Based Machine Learning" book by John Winn, implementing solutions using Pyro instead of the Infer.NET framework used in the book's companion code.

Key Features

• Implementation of Model-Based Machine Learning examples using Pyro
• Exploration of Pyro's capabilities in expressing complex probabilistic models

How to Use

1. Clone this repository
2. Install the environment using Miniconda:

   conda env create -f environment.yml
   conda activate pyro

Alternatively, click the badge below to launch this project in a Binder environment in your browser.

Notebooks

1. Chapter 1 - Implementation of first chapter examples using Pyro.
2. Chapter 2 - Implementation of second chapter examples using Pyro.

Technologies Used

• Python 3.x
• Pyro 1.4.0
• Jupyter Notebook
• PyTorch
• Matplotlib

Future Work

• Implement more examples from the book
• Implement all examples in NumPyro

How to Contribute

This is a personal learning project, but suggestions and discussions are welcome! Feel free to open an issue or submit a pull request.

Status

This project under sporadic development. Content and implementations may change as I progress through the book and deepen my understanding of Pyro.

Resources

• Pyro Documentation
• Model-Based Machine Learning Book