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Marclay is decidedly modelling a Time that is not a smooth and continuous current at all.
We have done this by modelling a time preference rate.
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However, it might be advantageous to model a time series around rough estimates of the "true" delays.
In general, channels with random states can model a time-varying wireless channel as well as interfering signals.
However, because we did not model a time-dependent slope, further studies are necessary to identify its effect on the estimation quality.
First, we use WINNER II [16] scenario for modelling a fast time-varying channel.
The model differs from Cohen/Murray because only one MDR strain is modeled at a time [17].
Driving forces are modeled using a time discretization.
To improve the TWIM into a real-time model, a time series autoregressive model to forecast short-term TWIM coefficients is also developed (Tsai et al. 2014a).
As mentioned above, for adequate ARIMA modeling, a time series should be stationary with respect to mean and variance.
For many other animal models, a time reference gene is not (yet) available and other embryo properties are applied to estimate the time of development.
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