Tutorials#
Included below are tutorials introducing Gaussian processes and their extension to 2D data sets.
The optimised method used by luas to calculate the log-likelihood and its derivatives is first demonstrated using an implementation in NumPy in “An Introduction to 2D Gaussian Processes”. This should be useful if you want to understand the mathematics behind the optimisation in LuasKernel but isn’t necessary to use luas.
If you would like to see a sample analysis using luas then there are examples demonstrating an analysis of synthetic spectroscopic transit light curves containing time and wavelength correlated noise using either PyMC or NumPyro as the choice of inference library.