"There are really two major branches of predictive mathematics:
- The first is more statistical and normative, and seeks to model data on specific and identifiable variables and factors. When it’s successful, it not only is able to predict what happens, but it’s able to EXPLAIN the prediction in plain English. We know not only what is likely to happen, but why it’s likely to occur.
- The other model is called neural networking, and seeks to emulate how the human brain learns via a network of associations, strengthened or weakened according to trial and error learning. When this model succeeds, it’s often not able to explain WHY it can predict the data (just like we can’t always explain our favorite recipes beyond “a pinch of this and a pinch of that”), but it’s usually a lot more powerful in it’s predictive accuracy. (Just like your grandmother’s apple pie tastes so much better than anyone else trying to follow a strict recipe). (Dr.Glenn Livingstone, PhD)