To use nongaussianity in ICA estimation, we must have a quantitative
measure of nongaussianity of a random variable, say *y*.
To simplify things, let us assume that *y* is centered (zero-mean) and
has variance equal to one. Actually, one of the functions of
preprocessing in ICA algorithms, to be covered in Section 5, is to make this simplification possible.