Therefore, the resulting controller does not depend explicitly on the Lyapunov matrix, being robust with respect to the uncertain transition rates.
Using this approach, we found that transcription factor activities predicted by NCA and our original connectivity matrix were robust, even if 10 15% of the connectivity matrix contained inaccurate connections (Table 1).
Effectively, our procedure seeks an autoencoding basis for the observed matrix that is robust with respect to the specified noise model.
(Note that the specific ordering depends on HSPG concentrations, protease levels, degradation rates, etc. Given their intrinsic variability in biological systems, the conclusion that a sufficiently strong matrix-binding isoform will have less matrix-bound VEGF is robust).
The reason is that the TBD matrix is more robust to quantization noise based on the fact the TBD matrix can effectively restrict the impact of quantization on speech signals.
In addition, we find that the TBD matrix is more robust to the quantization noise than the dense Gaussian matrix based on the fact that the TBD matrix can effectively restricted the impact of quantization noise on reconstruction of speech signals.
TOM based distance matrix is a robust and powerful measurement in building co-expression network.
If this is the case, it is not hard to image why the G matrix is so robust in its prediction ability and why we can successfully apply the same G matrix or numerator relationship matrix (A matrix (Henderson 1975b)) to genetic evaluation for different traits.
Anomalous matrices can correct overextension because alignment boundary correction is robust to matrix selection.
We used a compound symmetry covariance structure in fitting the model; we estimated SEs and tests using an estimated covariance matrix of parameter estimates that is robust to misspecification of the form of the covariance structure.
