|
A lot of this is done in discrete math. You know, the actual probability is defined by this integral, but there is no closed form solution to the integral, so we do sums to find the approximate answer. Anyone can understand sums. And, it's probabilities, so the sums must equal one. Not that hard, right ;) It sure helps to understand the integral equations, especially if you want to read the original literature. But realistically you are going to need to understand summing, normalizing, algorithms for clustering, and so on. You probably don't want to write your own numerical code anyway; someone else did it, and they handled all the edge cases that a naive implementation misses. You can find PDFs of the James, Witten, Hastie, Tibshirani book "An Introduction to Statistical Learning" [1]. Scroll on through - there is nothing intimidating math wise. All the heavy lifting is left to R. Jump in, the water is fine! [1] http://web.stanford.edu/~hastie/pub.htm |
http://statweb.stanford.edu/~tibs/ElemStatLearn/