Pyro-Study

Introduction

This repository contains the notebook files implementation for Pyro tutorial. Official Pyro tutorial here: Official Tutorial
Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling. It was designed with these key principles:
- Universal: Pyro can represent any computable probability distribution.
- Scalable: Pyro scales to large data sets with little overhead.
- Minimal: Pyro is implemented with a small core of powerful, composable abstractions.
- Flexible: Pyro aims for automation when you want it, control when you need it.

Name		Name	Last commit message	Last commit date
Latest commit History 67 Commits
Matching		Matching
Normalizing Flow		Normalizing Flow
An_Introduction_to_Models_and_Inference_in_Pyro.ipynb		An_Introduction_to_Models_and_Inference_in_Pyro.ipynb
BCCA_Normalizing_Flow_via_PyTorch_implementation.ipynb		BCCA_Normalizing_Flow_via_PyTorch_implementation.ipynb
BCCA_Re_implementation.ipynb		BCCA_Re_implementation.ipynb
Copy_of_GMM_modification.ipynb		Copy_of_GMM_modification.ipynb
Flows + BCCA.ipynb		Flows + BCCA.ipynb
GMM_modification.ipynb		GMM_modification.ipynb
Gaussian Mixture sample flow.jpg		Gaussian Mixture sample flow.jpg
Gaussian version of AT and IAT.png		Gaussian version of AT and IAT.png
Gaussian_Mixture_Model.ipynb		Gaussian_Mixture_Model.ipynb
Generative model component of VAE.png		Generative model component of VAE.png
IAF structure.png		IAF structure.png
Mini_Pyro.ipynb		Mini_Pyro.ipynb
Poutine_A_Guide_to_Programming_with_Effect_Handlers.ipynb		Poutine_A_Guide_to_Programming_with_Effect_Handlers.ipynb
README.md		README.md
SVI_Part_I_An_Introduction_to_Stochastic_Variational_Inference_in_Pyro.ipynb		SVI_Part_I_An_Introduction_to_Stochastic_Variational_Inference_in_Pyro.ipynb
VAE Structure.png		VAE Structure.png
Variational_Autoencoders.ipynb		Variational_Autoencoders.ipynb
pyro document.pdf		pyro document.pdf
variance_inference.ipynb		variance_inference.ipynb
working flow.png		working flow.png