Timeline for Distributions of eigenvalues for matrix normal distribution: related references
Current License: CC BY-SA 3.0
11 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Dec 20, 2013 at 9:26 | comment | added | jkt | @oferzeitouni, thanks a lot for the references. | |
| Dec 20, 2013 at 9:18 | comment | added | ofer zeitouni | So you are dealing with square matrices. This slightly simplifies matters. Not worrying about issues of regularity, the answer should be the given by the Brown measure of your matrix, which can be computed. See the [paper ](arxiv.org/pdf/math/9912242.pdf) for details, and [arxiv.org/pdf/0909.2214v2.pdf] and [arxiv.org/pdf/1208.5100.pdf] for examples where the convergence is proved. | |
| Dec 20, 2013 at 8:24 | comment | added | jkt | Though, I should note that U does not need to be structured for my problem. For this specific figure, U was $0.5I_{6 \times 6}$. | |
| Dec 20, 2013 at 8:21 | comment | added | jkt | @oferzeitouni, please see the edit. U is a scaled identity matrix, V is arbitrary. | |
| Dec 20, 2013 at 8:20 | history | edited | jkt | CC BY-SA 3.0 |
added matrices M, U and V
|
| Dec 20, 2013 at 7:37 | comment | added | ofer zeitouni | What are U and V in the plot you give? the identity matrix? | |
| Dec 20, 2013 at 5:46 | history | edited | jkt | CC BY-SA 3.0 |
minor edit
|
| Dec 20, 2013 at 5:44 | comment | added | jkt | @oferzeitouni , If you can check the wikipedia link for the matrix normal distribution, M is the mean matrix, U is the covariance amongst the rows and V is the covariance amongst the columns. | |
| Dec 19, 2013 at 15:30 | comment | added | ofer zeitouni | what are U and V? and what is M in the figure? | |
| Dec 19, 2013 at 6:02 | history | edited | jkt | CC BY-SA 3.0 |
minor formatting
|
| Dec 19, 2013 at 4:54 | history | asked | jkt | CC BY-SA 3.0 |