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Tests results were involved statistical analysis, obtained regression models of dependent variables E1, E2 demonstrate high prediction capability; relative error in predicting of models is ± 25%.
For example, concerns that correlations can be influenced by how the absences are drawn to fit presence-absence models led Schweiger et al. (2008, 2011) to use only the range of the host plant species to select absences for calibrating their models of dependent butterfly species.
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Then, Section 2 introduces a genuine model of dependent sources, for which separation is possible.
We now introduce a new statistical model of dependent sources, which consists in a probability mixture of sources.
We have then introduced a model of dependent sources which consists in a probability mixture of two distributions.
A DE is replaced producing sum of fractional polynomial derivative terms, forming a system model of dependent variables.
D-PNN constructs a general partial differential equation, which defines a system model of dependent variables, applying integral fractional polynomial sum terms.
Surprisingly, Hofmann and Streicher 1996 designed a model of dependent type theory where this is not valid, that is a model where they can be different proofs that two elements are equal.
Differential polynomial neural network is a new type of neural network developed by the author, which constructs and resolves an unknown general partial differential equation, describing a system model of dependent variables.
MRF methods provide the framework for probabilistic modeling of dependent random variables.
This is an extension of simple regression modeling of dependent variable on independent variables to show associations between variables.
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CEO of Professional Science Editing for Scientists @ prosciediting.com