Companion Notebook: Transforming Data to a Gaussian Distribution

The multivariate Gaussian distribution is central to computing conditional probabilities and simulating continuous variables, which requires transforming each variable to a univariate Gaussian distribution with the normal score transform.

The original Geostatistics Lesson by Michael Pyrcz and Clayton Deutsch remains a core reference. To show the practical application, Haoze Zhang, Ben Harding and Ryan Barnett prepared a Python companion notebook that walks through the normal score transformation and back-transformation, including implementation details such as despiking and distribution smoothing.