which is the 'meann square minus the mean squared". We think of the variance as the power of the non-constant signal components (i.e., everything but dc).
We propose a new criteria called normalized eigenvalue variance as the measure of the convergence rate of a large scale network system with consensus.
Second-phase sampling patterns optimizing the proposed criterion lead to predictions closer to the true image than when using the kriging variance as the main criterion.
This correspondence happens, in part, as the scientists in question often bracket the environment while privileging racialized genetic variance as the primary source of health disparities for common disease, in this case between Mexicans and Puerto Ricans with asthma.
It was generated as a test of Dean Radin's prediction that the variance would show strong fluctuations: I'd predict something like ripples of high and low variance, as the emotional shocks continue to reverberate for days and weeks.
Our procedure achieves the same bias and variance as the oracle estimator based on knowing the other components, and in this sense improves on the method analyzed in Opsomer and Ruppert (1997).
We can also think of variance as the model complexity or, equivalently, memory capacity of the learning method - how detailed a characterization of the training set it can remember and then apply to new data.
It is demonstrated that the estimate produced by the designed filter generates the mean-square estimate, which has the same minimum estimation-error variance as the best estimate given by the classical Kalman Bucy filter.
We treat the marginal posterior of all the unknown quantities except the spline coefficients and error variance as the target distribution to reduce the dimension of the parameters and to obtain a rapid algorithm.
However, in the situation of unreplicated designs, most methods known so far rely on the assumption of constant variances, or a functional relationship between mean and variance as the only source of heteroscedasticity.
It is shown that the designed sliding mode mean-square filter generates the mean-square estimate, which has the same minimum estimation error variance as the estimate given by the conventional mean-square polynomial filter Basin et al. (2008) [8], although the gain matrices of both filters are different.
