The parity bits and statistical modelling of the correlation noise are the input of the channel decoders.
A reliable model of the correlation between the physicochemical properties of the APIs and the tablet properties was thus constructed.
The Kalman predictor requires statistical models of the correlation properties of the channels over time and frequency to adjust the channel estimate according to the short-term fading.
All rate/quality allocation methods presented above are similar in the sense that they use an encoder-derived model of the correlation noise between the SI and the WZ frame to determine the rate or the quantization parameters.
In this way, we can obtain R xx ¯ using the model of the correlation matrix developed by [16] and used subsequently by [17], which is given by: R xx ¯ α = α I + 1 − α u u H, (17).
Moreover, the higher performance of our method might be due to the explicit modelling of the correlation between image features.
We aim to investigate this strategy, and use statistical models of the correlation values to determine the weighting factors.
In co-expression analysis, the correlation between pairs of genes is typically combined into a network model of the correlation structure, which facilitates secondary network analysis such as community structure or centrality [ 1].
The use of repeated-measures ANOVA allows for the modeling of this correlation, which is necessary to have standard errors calculated correctly.
The networks of functional interconnections are then found by comparing the correlations among these regions against a model of the correlations in the noise.
