Each conditional mean is the Bayes optimal (minimum mean squared error) estimate given the specific local context.
Each conditional mean is the Bayes optimal (minimum mean squared error) estimate given the specific local context.
The overall bias is +0.05 m/s relative to NCEP-ADP observations, and the RMSE (Root Mean Squared Error) is 0.93 m/s.
Keeping each portion as test data, we build the model on the remaining (k-1 portion) data and calculate the mean squared error of the predictions.
Survey researchers are able to obtain a good quantitative estimated of the variance component of mean squared error.
A mean square error of the training signal is minimized while estimating the transmitted signal, until the mean square error is less than a predetermine threshold (T).
Composite estimates having minimum variance and minimum mean square error are compared.
This is called the mean square error (MSE) of the regression.
Thus, a blind adaptive space-time equalization on the signal based on minimum mean square error (MMSE) may be provided.
In our individual model for OD, RSquare is 0.84 and the root mean square error is 1.12.
Three statistics are used in Ordinary Least Squares (OLS) regression to evaluate model fit: R-squared, the overall F-test, and the Root Mean Square Error (RMSE).
Sample less than 100 could lead to increased over-rejection rates for indices of goodness of fit such as the root mean square error of approximation (RMSEA).
The vertical accuracy of DEMs is expressed as the root mean square error (RMSE) of a sample of at least 28 elevation points.
A mean-squared error (MSE) for each tile is calculated.
In addition, this specification describes an MMSE (minimum mean-squared error) method that improves on the spectral estimation method.
The erasure slicer threshold may be based on signal-to-noise ratio (SNR) or mean-squared-error (MSE).
Then, one of the first and second antennas with a lower MSE is selected to receive and process the incoming signals.
An EMV measurement unit (104) measures an EVM of a reception signal and obtains its weighted average to derive an MSE.
root mean square phase error
RMS phase error
loop mean-square phase error
loop mean-square phase error
The estimated noise is in terms of a minimum mean-squared error.
r.m.s.tolerance error across aperture
This can be accomplished by utilizing an average of m (m = 2 or more) consecutive TA values, which will on average have a smaller mean-squared error than using a single TA value.
The LME model has the smallest average prediction errors.
An adaptive Decision Feedback Equaliser (DFE) is trained using the Delayed Least Mean Square (DLMS) algorithm.
A cost function establishes a mean-square error associated with the estimated channel impulse response as compared to a desired impulse response signal.
A cost function establishes a mean-square error associated with the estimated channel impulse response as compared to a desired impulse response signal.
information about quality criteria for this method (e.g. correlation coefficient, error sum of squares, etc.),
An r.m.s. error of 15 m in terrain height data has been found acceptable for many purposes.
information about quality criteria for this method (e.g. correlation coefficient, error sum of squares, etc.),
— information about quality criteria for this method (e.g. correlation coefficient, error sum of squares, etc.),
Requêtes fréquentes français :1-200, -1k, -2k, -3k, -4k, -5k, -7k, -10k, -20k, -40k, -100k, -200k, -500k, -1000k,
Requêtes fréquentes anglais :1-200, -1k, -2k, -3k, -4k, -5k, -7k, -10k, -20k, -40k, -100k, -200k, -500k, -1000k,
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