Finding the Best Fit: Bayesian Statistics Meets Geometry


Guest Commentary by Dr. Martyn Rittman

A common problem faced in research is when a set of data has been collected on the one hand, and a theoretical model (a set of equations with input parameters) exists on the other. We want to know firstly, whether the model describes the data well and secondly, what variables of the model give the best possible fit.

Continue reading