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Data transformation (statistics) A scatterplot in which the areas of the sovereign states and dependent territories in the world are plotted on the vertical axis against their populations on the horizontal axis. The upper plot uses raw data. In the lower plot, both the area and population data have been transformed using the logarithm function.
The graph forms a rectangular hyperbola. In mathematics, a multiplicative inverse or reciprocal for a number x, denoted by 1/ x or x−1, is a number which when multiplied by x yields the multiplicative identity, 1. The multiplicative inverse of a fraction a / b is b / a. For the multiplicative inverse of a real number, divide 1 by the number.
The reciprocal 1/ X of a random variable X, is a member of the same family of distribution as X, in the following cases: Cauchy distribution, F distribution, log logistic distribution . Examples: If X is a Cauchy ( μ, σ) random variable, then 1/ X is a Cauchy ( μ / C, σ / C) random variable where C = μ2 + σ2.
Definitions. The Lineweaver–Burk plot derives from a transformation of the Michaelis–Menten equation , in which the rate is a function of the substrate concentration and two parameters , the limiting rate, and , the Michaelis constant. Taking reciprocals of both sides of this equation it becomes as follows:
In probability and statistics, the reciprocal distribution, also known as the log-uniform distribution, is a continuous probability distribution. It is characterised by its probability density function, within the support of the distribution, being proportional to the reciprocal of the variable. The reciprocal distribution is an example of an ...
Reciprocal lattice. The computer-generated reciprocal lattice of a fictional monoclinic 3D crystal. A two-dimensional crystal and its reciprocal lattice. In physics, the reciprocal lattice emerges from the Fourier transform of another lattice. The direct lattice or real lattice is a periodic function in physical space, such as a crystal system ...
The light circles represent atoms. In condensed matter physics, Bloch's theorem states that solutions to the Schrödinger equation in a periodic potential can be expressed as plane waves modulated by periodic functions. The theorem is named after the Swiss physicist Felix Bloch, who discovered the theorem in 1929. [1]
In representation learning, knowledge graph embedding ( KGE ), also referred to as knowledge representation learning ( KRL ), or multi-relation learning, [1] is a machine learning task of learning a low-dimensional representation of a knowledge graph 's entities and relations while preserving their semantic meaning.