3 сент. 2013 г. · A matrix A is positive semi-definite if there is no vector z such that z′Az<0.

21 нояб. 2015 г. · My understanding is that positive definite matrices must have eigenvalues >0, while positive semidefinite matrices must have eigenvalues ≥0.

25 нояб. 2014 г. · So if a matrix is supposed to be a correlation matrix, it should be positive semi-definite. Note that the semi-definite is important here ...

22 апр. 2013 г. · Every covariance matrix is Positive semi-definite. That means every covariance matrix must have non-negative eigen values.

22 мар. 2013 г. · Indeed, one can say that the sample covariance matrix S is always positive and semi-definite because it can be seen as the variance of a ...

1 апр. 2015 г. · The fact that you have a negative eigen value means the matrix is indefinite which means that the correlations specified are not jointly feasible.

14 июн. 2012 г. · The covariance matrix is not positive definite because it is singular. That means that at least one of your variables can be expressed as a linear combination ...

13 авг. 2014 г. · It is well-known that a covariance matrix must be semi-positive definite, however, is the converse true? That is, does every semi-positive definite matrix ...

16 сент. 2010 г. · I would like to be able to efficiently generate positive-semidefinite (PSD) correlation matrices. My method slows down dramatically as I increase the size of ...

2 июн. 2020 г. · In other words, if a covariance matrix is positive definite, variability exists on all variables no matter how we transform the data. Is this ...