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| bio | website | |
|---|---|---|
| location | ||
| age | ||
| visits | member for | 2 years, 4 months |
| seen | May 2 at 17:28 | |
| stats | profile views | 10 |
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Jan 30 |
comment |
Efficient Cholesky decomposition of inverse matrix I'm doing the same thing, but I have been getting funky results. You can take the cholesky decomposition of $\Sigma^{-1}$ and find the upper cholesky decomposition of that and then take the inverse. However, what I am left with is a triangular matrix that can reproduce $\Sigma$, but it isn't the same as $chol(\Sigma)$ |
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Jan 28 |
comment |
How to generate a random matrix whose eigenvalues are less than one I've been beating my head against the wall trying to do a similar thing. My application is ensuring Bayesian VAR results are stationary in a MCMC, which I think is a slightly different set up. |
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Sep 12 |
comment |
Finding $X$ When $Y=XX'$ @BaronVT I see your point now. What kinds of conditions would need to be made on the eigenvalues. In the particular ones I'm working with, it will often be the case that several will be equal to each other. |
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Sep 12 |
comment |
Finding $X$ When $Y=XX'$ @MichaelHardy Thanks. |
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Sep 11 |
comment |
Finding $X$ When $Y=XX'$ $Y$ is some known matrix. It could be an identity matrix, or it could be anything else so long as it is positive definite. |
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Sep 11 |
asked | Finding $X$ When $Y=XX'$ |
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May 29 |
awarded | Supporter |
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Dec 27 |
awarded | Teacher |
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Nov 30 |
revised |
Block Diagonal Matrix Transformations added 615 characters in body |
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Nov 30 |
asked | Block Diagonal Matrix Transformations |
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Feb 17 |
answered | Conformability and Matrix Derivatives |
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Feb 17 |
awarded | Editor |
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Feb 17 |
revised |
Conformability and Matrix Derivatives added 1 characters in body |
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Feb 17 |
awarded | Student |
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Feb 17 |
asked | Conformability and Matrix Derivatives |
