This repository contains jupyter notebooks demonstrating the use of Bayesian techniques for parameter estimation. Specifically, we demonstrate python implementations of the following methods:
- Bayesian Linear Regression
- Grid Search
- Gibbs Sampling
- Metropolis Hastings
- Laplace approximation
- Variational Inference
These methods are covered in four python notebooks:
Progress is being made on Julia code and will continue to be updated.