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Variation varies between 0 and 1. Variation is 0 if and only if all cases belong to a single category. Variation is 1 if and only if cases are evenly divided across all categories. [1] In particular, the value of these standardized indices does not depend on the number of categories or number of samples.
[11] [12] [13] Variant interpretation and classification is notably subjective, as laboratories developed their own criteria prior to the ACMG-AMP guidelines. [ 8 ] [ 9 ] [ 14 ] This subjectiveness is further problematic when there is evidence that variant significance changes over time. [ 13 ]
Calculus of variations is concerned with variations of functionals, which are small changes in the functional's value due to small changes in the function that is its argument. The first variation [l] is defined as the linear part of the change in the functional, and the second variation [m] is defined as the quadratic part. [22]
Often, variation is quantified as variance; then, the more specific term explained variance can be used. The complementary part of the total variation is called unexplained or residual variation ; likewise, when discussing variance as such, this is referred to as unexplained or residual variance .
Author: pwei: Short title: Library of Congress Classification Outline; Date and time of digitizing: 16:07, 12 March 2003: File change date and time: 13:31, 22 November 2010
That is, for a random variable , the coefficient of variation of + is equal to the coefficient of variation of only when =. In the above example, Celsius can only be converted to Fahrenheit through a linear transformation of the form a x + b {\displaystyle ax+b} with b ≠ 0 {\displaystyle b\neq 0} , whereas Kelvins can be converted to Rankines ...
The calculation of prior probabilities depends on available data from the genome being studied, and the type of analysis being performed. For studies where good reference data containing frequencies of known mutations is available (for example, in studying human genome data), these known frequencies of genotypes in the population can be used to estimate priors.
Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning.They are typically used in complex statistical models consisting of observed variables (usually termed "data") as well as unknown parameters and latent variables, with various sorts of relationships among the three types of random variables, as ...