PyMC is a powerful tool for Bayesian modeling, but its integration with modern deep learning techniques remains underexplored.
This project aims to enhance PyMC by incorporating advanced neural network capabilities, such as variational inference or likelihood-free methods, using frameworks like PyTorch. I’ll solve this by developing a seamless interface between PyMC’s probabilistic framework and neural network architectures, improving model flexibility and performance for complex ML tasks.
My approach involves studying the PyMC codebase, prototyping NN integration, optimizing sampling compatibility, and delivering a user-friendly tutorial. Deliverables include:
- A functional NN integration module,
- comprehensive unit tests,
- detailed documentation, and
- an example showcasing a Bayesian neural network application.
This will empower PyMC users to tackle cutting-edge probabilistic ML problems efficiently.
Applicant: Avinash Bhargav Bandi
Email: avinashbhargavbandi@gmail.com