31. The covariance matrix adaptation ( CMA ) is a method to update the covariance matrix of this distribution. 32. The update equations for mean and covariance matrix maximize a likelihood while resembling an expectation-maximization algorithm. 33. In a mean-variance optimization framework, accurate estimation of the variance-covariance matrix is paramount. 34. In a practical application in portfolio optimization, accurate estimation of the variance-covariance matrix is paramount. 35. Similarly, random vectors whose covariance matrix is zero in every entry outside the main diagonal are called uncorrelated. 36. In an MPT or mean-variance optimization framework, accurate estimation of the variance covariance matrix is paramount. 37. What if the covariance matrix is not known a-priori and needs to be estimated from the data? 38. For example, in the case of a Gaussian distribution, this comprises the mean and the covariance matrix . 39. If X is a positive semi-definite square matrix, commonly referred to as the variance-covariance matrix . 40. Assuming the missing data are missing at random this results in an estimate for the covariance matrix which is unbiased.
