Moment of Inertia
The variance of a probability distribution is analogous to the moment of inertia in classical mechanics of a corresponding mass distribution along a line, with respect to rotation about its center of mass. It is because of this analogy that such things as the variance are called moments of probability distributions. The covariance matrix is related to the moment of inertia tensor for multivariate distributions. The moment of inertia of a cloud of n points with a covariance matrix of is given by
This difference between moment of inertia in physics and in statistics is clear for points that are gathered along a line. Suppose many points are close to the x axis and distributed along it. The covariance matrix might look like
That is, there is the most variance in the x direction. However, physicists would consider this to have a low moment about the x axis so the moment-of-inertia tensor is
Read more about this topic: Variance
Famous quotes containing the words moment and/or inertia:
“From the moment a child begins to speak, he is taught to respect the word; he is taught how to use the word and how not to use it. The word is all-powerful, because it can build a man up, but it can also tear him down. That’s how powerful it is. So a child is taught to use words tenderly and never against anyone; a child is told never to take anyone’s name or reputation in vain.”
—Henry Old Coyote (20th century)
“What is wrong with priests and popes is that instead of being apostles and saints, they are nothing but empirics who say “I know” instead of “I am learning,” and pray for credulity and inertia as wise men pray for scepticism and activity.”
—George Bernard Shaw (1856–1950)