Welcome to the Gaussian Processes Section!

Here we demonstrate tasks for which GPs are suitable, and examine their advantages and disadvantages.

Current Implementations

• Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data [paper]

• Variational Learning of Inducing Variables in Sparse Gaussian Processes [paper]

Installation

Note: when code blocks are prefixed with >, it means the command should be preformed in the terminal.

1. Clone the repository:

> git clone https://github.com/sbrml/gaussian_processes.git

2. Create a virtual environment in the repo's folder, and activate it:

> cd gaussian_processes
> python3 -m venv gp_venv
> source gp_venv/bin/activate

3. Install the requirements:

(gp_venv)> pip install --upgrade pip 
(gp_venv)> pip install -r requirements.txt 

4. Run a Jupyter Notebook:

(gp_venv)> jupyter notebook

The notebooks can be found in the code folder.