Titlebook: An Introduction to Statistical Data Science; Theory and Models Giorgio Picci Textbook 2024 The Editor(s) (if applicable) and The Author(s),

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https://doi.org/10.1007/978-3-642-39476-8 is an a priori information of probabilistic nature about the variable . which is the object of the statistical inference problem. making it a . which, by its very nature, cannot be assigned an exact numerical value. Many problems in econometrics and engineering have a natural formulation in the Bay

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The Question Concerning Technology as Artn, which is for example relevant to scene and motion reconstruction in computer vision. A geometric formulation points to a generalization to spherical manifolds of the familiar Gaussian inference on linear-spaces. Next we discuss in some detail non-linear support vector machines which is a very imp

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https://doi.org/10.1007/978-3-642-39473-7 observations indexed by time. Due to errors and various causes of uncertainty these data are random and It is therefore reasonable to model them as .. The scope of the statistical exercise is to discover a stochastic mathematical model of the underlying physical or economic dynamical system for the

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Nicolas Jones,Armelle Brun,Anne Boyers from the outset. The technique to solve the problem turns essentially out to be just least squares which, for Gaussian data, can be directly justified based on the maximum likelihood principle. We warn the reader that this is however true only if it is based on strong a priori assumption of noiseless output data and suggest a wide perspective.