The multivariate normal distribution is a statistical distribution that generalizes the normal distribution to multiple dimensions. It describes the behavior of a vector of correlated random variables, where each variable follows a normal distribution. The shape of this distribution is often represented as an ellipsoid in multidimensional space, with its orientation and size determined by the means and variances of the variables, as well as their covariances. In a multivariate normal distribution, the mean vector indicates the center of the distribution, while the covariance matrix captures the relationships between the variables. This distribution is widely used in various fields, including finance, biology, and machine learning, to model complex data sets with interdependent variables.