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السنة أسم المؤلف أسم الكتاب ت 1. 2006 Michael Farr 100 Fastest
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... A History of Parametric Statistical Inference from Bernoulli to Fisher, 17131935, A History of Probability and Statistics and Their Applications before 1750 A Kalman Filter Primer (Statistics Textbooks and Monographs) A Mathematician Plays the Stock Market A Matrix Handbook for Statisticians A Moder ...
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... It overfits a lot. Therefore we train on 224x224 patches extracted randomly from 256x256 images, and also their horizontal reflections. ...
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... The sex and education interaction term is seen to be highly significant, as can be seen from the summary output in Figure 6.7. We can obtain a plot of deviance residuals plotted against fitted values using the following code above Figure 6.9. The residuals fall into a horizontal band between −2 and ...
< 1 ... 90 91 92 93 94 95 96 97 98 ... 178 >

Data assimilation

Data assimilation is the process by which observations are incorporated into a computer model of a real system. Applications of data assimilation arise in many fields of geosciences, perhaps most importantly in weather forecasting and hydrology. The most commonly used form of data assimilation proceeds by analysis cycles. In each analysis cycle, observations of the current (and possibly past) state of a system are combined with the results from a numerical model (the forecast) to produce an analysis, which is considered as 'the best' estimate of the current state of the system. This is called the analysis step. Essentially, the analysis step tries to balance the uncertainty in the data and in the forecast. The result may be the best estimate of the physical system, but it may not the best estimate of the model's incomplete representation of that system, so some filtering may be required. The model is then advanced in time and its result becomes the forecast in the next analysis cycle. As an alternative to analysis cycles, data assimilation can proceed by some sort of nudging process, where the model equations themselves are modified to add terms that continuously push the model towards observations.
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