Sentence examples for measured sample eigenvalues from inspiring English sources

One particular candidate could be the measured sample eigenvalues themselves.

One application is to use them to test whether the candidate population eigenvalue sets match with the measured sample eigenvalues.

The sample eigenvalues corresponding to these population eigenvalues ( L ̂ n ) are estimated and compared to the measured sample eigenvalues (L).

If this estimated sample eigenvalue density does not match the empirical density of the measured sample eigenvalues, the candidate population eigenvalue set is probably not a good candidate.

If they do not match, then the measured sample eigenvalues are probably considerably biased estimates of the original population eigenvalues as well.

As noted earlier, the more common problem is how to get from the measured sample eigenvalues an estimate of the population eigenvalues.

We therefore convolve the empirical distribution of the measured sample eigenvalue set with the Cauchy density, resulting in g y (l), and compare these two densities.

From the population eigenvalue sets and the measured sample eigenvalue sets, we determined the empirical eigenvalue distribution function, which is given by Equation 3 for an eigenvalue set {x k |k = 1…p}: F p ( x ) = 1 p ∑ k = 1 p u x - x k, (3).

Measured sample's density.

In the example we measured the sample eigenvalues of synthetic data, with the population eigenvalues uniformly distributed between 1 and 3. To show that the (GSA) limit 'if p → ∞' is relevant we set p to 6,20, and 100, while keeping γ = 1 3, so N = 18,60, and 300, respectively.

The estimate of the population eigenvalues is updated by comparing these sample eigenvalues with the measured eigenvalues.