Expert: Chuck Cosby - 4/11/2009

Question
Hi
i want to know about the orthogonality,could you plz explain it to me with an example?
i search through internet but unfortunately it doesn't help,and i still feel confusing about the function of orthogonality

Thanx
Bita

Answer
"In statistics and signal processing, the orthogonality principle is a necessary and sufficient condition for the optimality of a Bayesian estimator. Loosely stated, the orthogonality principle says that the error vector of the optimal estimator (in a mean square error sense) is orthogonal to any possible estimator. The orthogonality principle is most commonly stated for linear estimators, but more general formulations are possible. Since the principle is a necessary and sufficient condition for optimality, it can be used to find the minimum mean square error estimator."

I got this paragragh from Wikipedia.  i don't use this term as it seems to be related to speech recogintion and my area is natural language processing.