This repository summaries basic principles and technologies in Probabilistic Graphical Models and uses Gaussian Mixture Models as an example to illustrate these basic ideas. Comments and discussion are highly appreciated!

• Representation
• Inference
• Learning

with NumPy & SciPy:

• Maximize Likelihood Estimation by EM Algorithm
• Maximize Likelihood Estimation by Stochastic EM Algorithm
• Data Augmentation Algorithm
• Mean Field Variational Inference
• Gibbs Sampling
• Metropolis-Hastings Sampling

with PyTorch & Pyro:

• [PyTorch] Maximize Likelihood Estimation by Gradient Descent
• [PyTorch] Hamiltonian Monte Carlo
• [Pyro] Stochastic Variational Inference