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Slide 1
Slide 1

Applied Statistical Modeling and Inference
Applied Statistical Modeling and Inference

... Course  Description  (~250  words  or  less):   This  is  a  course  in  intermediate  and  advanced  statistical  inference  techniques  in  the  context  of  applied   research  questions  in  data  science.    Assuming  some  prior  ex ...
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...  Solution I: error guessing and estimation.  Idea 1: using observable statistical traits from the model itself to guess the error on unlabeled streaming data.  Idea 2: using very small number of specifically acquired examples to statistically estimate the error – similar to estimate poll to estim ...
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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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