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The AP U.S. History course is designed to provide the same level of content and instruction that students would face in a freshman-level college survey class. It generally uses a college-level textbook as the foundation for the course and covers nine periods of U.S. history, spanning from the pre-Columbian era to the present day. The percentage ...
The ease of quantification is one of the features used to distinguish hard and soft sciences from each other. Scientists often consider hard sciences to be more scientific or rigorous, but this is disputed by social scientists who maintain that appropriate rigor includes the qualitative evaluation of the broader contexts of qualitative data.
A mean is a quantity representing the "center" of a collection of numbers and is intermediate to the extreme values of the set of numbers. [1] There are several kinds of means (or "measures of central tendency") in mathematics, especially in statistics.
As an example, the only difference in the definition of uniform continuity and (ordinary) continuity is the order of quantifications. First order quantifiers approximate the meanings of some natural language quantifiers such as "some" and "all". However, many natural language quantifiers can only be analyzed in terms of generalized quantifiers.
Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.
Because quantization is a many-to-few mapping, it is an inherently non-linear and irreversible process (i.e., because the same output value is shared by multiple input values, it is impossible, in general, to recover the exact input value when given only the output value).
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In machine learning and data mining, quantification (variously called learning to quantify, or supervised prevalence estimation, or class prior estimation) is the task of using supervised learning in order to train models (quantifiers) that estimate the relative frequencies (also known as prevalence values) of the classes of interest in a sample of unlabelled data items.