Sentence examples for expected squared error from inspiring English sources

Putting these pieces together, the expected squared error achieved by a 2-point correlator is a quadratic function of the power spectrum (33) ϵ = σ v 2 − 2 ∑ k = 0 ∞ γ k S k + ∑ k 1 = 0 ∞ ∑ k 2 = 0 ∞ Γ k 1 k 2 S k 1 S k 2. We're interested to know whether spatial correlations can enhance the accuracy of the 2-point correlator.

where we split up the MSE into two terms: the first term is closely related to the SNR of the reconstructed HDR image (see Equation (13)), the second term is the expected squared bias error.

The technique is based on finding a reduced-rank approximation to the autoregression operator that minimizes the expected squared norm of the prediction error.

Mean square error of prediction (MSEP) indicates expected squared Euclidian distance between predicted and observed phenotypes, R expressed the proportion of variance explained by the model and Q evaluates the prediction quality of the model.

This is equivalent to choosing the button that leads to the lowest expected mean squared error between the position indicated by the button and the position of the stimulus.

As expected, mean squared error (MSE) is positively associated with the small number of studies, and large heterogeneity in meta-analysis.

On the other hand local minimax learning provides such probability estimates with best finite sample bounds on expected mean squared error on an individual basis for each queried patient.

The dose optimization software then calculates the drug dose that minimizes the expected weighted squared error (over the Bayesian posterior distribution) between the predicted and user-specified target drug concentrations in the future data file.

If an analyst were to disregard the 'batches' component such that this variation is simply added to the 'mice within treatments' component, then the pooled error term would have an expected mean squared error of approximately σ + σm + 2σb, which would be estimated with 39 degrees of freedom.

In contrast, nonparametric population models, having multiple discrete support points which describe the entire model parameter distributions without having to make any assumptions about their shape, can easily compute the expected weighted squared error with which any dosage regimen fails to hit a desired target and can then find the regimen which specifically minimizes that error.

Given a design, the model allows one to incorporate the recognized sources of variation into expected mean squared errors for a statistical analysis.