Titlebook: Numerical Python; Scientific Computing Robert Johansson Book 20192nd edition Robert Johansson 2019 Python.numerical.NumPy.SciPy.computation

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Partial Differential Equations,e derivatives in the equation are . derivatives. As such they are generalizations of ordinary differential equations, which were covered in Chapter .. Conceptually, the difference between ordinary and partial differential equations is not that big, but the computational techniques required to deal w

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Data Processing and Analysis,ional work. Starting with this chapter, we move on to explore data processing and analysis, statistics, and statistical modeling. As the first step in this direction, we look at the data analysis library .. This library provides convenient data structures for representing series and tables of data a

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Statistics,s, medicine, and other fields where data is used for obtaining knowledge and making decisions. With the recent proliferation of data analytics, there has been a surge of renewed interest in statistical methods. Still, computer-aided statistics has a long history, and it is a field that traditionally

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Statistical Modeling,r and explore statistical modeling, which deals with creating models that attempt to explain data. A model can have one or several parameters, and we can use a fitting procedure to find the values of the parameter that best explains the observed data. Once a model has been fitted to data, it can be

Sleep-Paralysis

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Bayesian Statistics,ayesian statistics, in contrast to the frequentist’s statistics that we used in Chapter . and Chapter ., treats probability as a degree of belief rather than as a measure of proportions of observed outcomes. This different point of view gives rise to distinct statistical methods that we can use in p

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Signal Processing,ntext can be a quantity that varies in time (temporal signal) or as a function of space coordinates (spatial signal). For example, an audio signal is a typical example of a temporal signal, while an image is a typical example of a spatial signal in two dimensions. In reality, signals are often conti

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