This page contains the details of a technical writing project accepted for Google Season of Docs.
Project summary
- Open source organization:
- The Julia Language
- Technical writer:
- Liza
- Project name:
- Bayesian inference for Gaussian Processes
- Project length:
- Standard length (3 months)
Project description
I would like to develop (and teach myself) some easy-to-start material, allowing to perform Bayesian inference for Gaussian processes (GPs) using Julia's ecosystem.
Outline:
- What are non-parameteric models and, in particular, GPs
- One-dimensional curve fitting simple example, i.e. given a set of pairs (x_i, y_i) how to fit f(x)=y
- Discuss different kernels: squared exponential, Matern, linear, compositions
- A more involved 2d example, modelling spatial data