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Using transformations to graph functionsSometimes we can use the concept of transformations to graph complicated functions when we know how to graph the simpler ones.For example, if you know the graph of f(x), the graph of f(x) + c will be the same function, just shifted up by c ...
Exponential distribution, Gamma distribution, Logistic distribution, Weibull distribution. Less common continuous distributions — ones you’ll rarely encounter in basic statistics courses— include: The Shakil-Singh-Kibria distribution, based on theWhittaker functions[1]. ...
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on our exploration of the datasets, we subdivided markers as perfect (high coverage, AUROC >0.8, high signal-to-noise, FCd >8), specific (high signal-to-noise), sensitive (high coverage), or weak (DE, but low coverage and low signal-to-noise). As expected, the Pareto fronts ...
Classical approaches for time series analysis generally rely on statistical principles, including decomposition, auto-regression, differencing, exponential smoothing, and smooth transitioning functions [7, 45] (Diijk 2002). Machine learning approaches for time series analy- sis generally segment the series...
As could be guessed, the impact of levelling must somehow depend on characteristic lateral scale of the roughness and its ratio to the scan length L. Intuitively, if the scan is very long and the lateral scale of the rough- ness short, the estimated background will approach the true one...
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