Cumulative Mean and Variance

Computes cumulative the mean/variance of a vector X along an arbitrary dimension

>> Y = cummean(X,DIM);
if X is MxN, Y is also MxN. To illustrate the functionality, lets assume X is a 1xN vector. Then, Y is a 1xN vector where the n-th entry in Y is given by mean(X(1:n)). So, Y(end) = mean(X), and Y(1) = X(1). cumvar works in the same way, and this can be done on an arbitrary dimensional X along dimension DIM. See screenshot for an example.

These functions are useful (at least to me) for determining how many iterations of a process are required until the mean and variance of the process are stable (ie: not changing with increasing iterations). Maybe useful to people doing Markhov chain stuff?