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Fitting Gaussian Variograms in Python: A Guide to Spatial Analysis and Machine Learning.
This is the second article I am writing on fitting a variogram with Python only this time I want to focus on machine learning, in particular Gaussian which is a popular ML algorithm for kriging and fitting a variogram for spatial analysis.
Together with code samples, I will go over the actual procedures for fitting a Gaussian variogram using Python modules. I will showcase the usefulness of the fitted variogram in practical situations, I will show you how to incorporate it into kriging for spatial predictions.
This article will give you important insights into using Gaussian variograms for improved spatial analysis, regardless of your experience with geostatistics.
I have written extensively on geostatistics here, combining geostatistics and machine learning and fitting a variogram with Python.
Gaussian Model Explained
In general terms, Gaussian refers to the Gaussian distribution, also known as the normal distribution and a cornerstone of probability theory and statistics. The following are the salient features:
Bell-Shaped Curve: The Gaussian distribution is symmetric about its mean and has a bell-shaped curve. This indicates that probabilities taper off symmetrically…
