Titlebook: Advanced Data Analysis in Neuroscience; Integrating Statisti Daniel Durstewitz Textbook 2017 Springer International Publishing AG 2017 stat

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Bernstein Series in Computational Neurosciencehttp://image.papertrans.cn/a/image/145468.jpg

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https://doi.org/10.1007/978-3-662-40106-4In classification problems, the objective is to classify observations into a set of . discrete classes .∈{1….}. To these ends, one often tries to estimate or approximate the posterior probabilities .(.|.)?≡?.(.?=?.|.). Given these, one could classify new observations . into the class .. for which we have

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https://doi.org/10.1007/978-3-319-59976-2statistical methods in neuroscience; neural time series; multivariate statistics; machine learning; stat

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978-3-319-86750-2Springer International Publishing AG 2017

CHIDE

,Kommentar und Schlu?folgerungen,general principles and mathematical techniques for handling these. In this sense it will lay out some of the ground on which statistical methods developed in later chapters rest. It is assumed that the reader is basically familiar with core concepts in probability theory and statistics, such as expe

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,Kommentar und Schlu?folgerungen,ues are minimized (a so-called squared error loss function, see Eq. .). Then the regression function which optimally achieves this is given by .(.)?=?.(.|.) (Winer 1971; Bishop 2006; Hastie et al. 2009), that is the goal in regression is to model the conditional expectancy of . (the “outputs” or “re

Airtight

https://doi.org/10.1007/978-3-662-40106-4 in statistical model fitting or parameter estimation: We usually only have available a comparatively small sample from a much larger population, but we really want to make statements about the population as a whole. Now, if we choose a sufficiently flexible model, e.g., a local or spline regression

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