title: Gaussian basis function selection - Linear Regression
body: <p>I'm looking to set up a linear regression using 2D Gaussian basis functions. My input training variables cover a two dimensional space. Before applying the machine learning (Bayesian linear regression), I need to select parameters for the Gaussians - mean and variance and also decide how many basis functions to use. </p>
  
  <p>I am currently spacing the means (of a preallocated number of basis Gaussians) evenly over a grid, and just assuming constant variance. This is obviously not the best approach.
  Any ideas on how to calculate these variables? </p>