## Computer Science & Engineering →Pattern Recognition Lab
→List Of Experiments

# Mean and Covariance

The locations at which the samples of a class can appear in the feature space can be
statistically modeled using a distribution or density function. There are different
properties of the density function that allows us to gain an understanding of the
class under consideration. The most popular metrics are the *mean* and
*variance* (or *covariance matrix*). In the case of normal densities,
these two parameters completely characterize the density itself, and hence are
denoted as the parameters of the density: N(mean,cov).

In this experiment, we will try to gain an understanding of these metrics. Specifically, we need to understand the effect of these parameters on the shape of the data distribution under various assumptions of densities. This will allow us to visualize the nature of distribution, given the mean vector and the covariance matrix.