Wishart's theorem

Wishart's theorem is a statistical principle that describes the distribution of estimated sample covariance matrices. In simple terms, when you collect data points from several variables and calculate how they vary together, the Wishart theorem helps predict the behavior of these variances and covariances. It establishes that, under certain conditions, the matrix formed from these calculations follows a specific distribution, known as the Wishart distribution. This theorem is essential in multivariate statistics and plays a crucial role in areas such as finance, biology, and machine learning, where understanding relationships between multiple variables is important.