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PowerPoint - c

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... values of concentration of hormones as in clinical data. However, we must mention the important observation between the two clinical variables is that the TSH changes on the order of hours and FT4 changes on the order of days in the blood. In Fig. 3, we have unstable behaviour of considered system ...
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... b = PX Y and in the regression context it is especially common As always Y to call PX = H the hat matrix. It is n × n and its diagonal entries hii are sometimes used as indices of “influence” or “leverage” of a particular case on the regression fit. It is the case that each hii ≥ 0 and X hii = trace ...
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... is, the instantaneous risk of failing at a given time from a given cause, among all individuals at risk at that time. The joint distribution can also be specified through the cumulative incidence function, representing the probability of failing from a given cause before a specific time. These two f ...
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07 Data Abstraction

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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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