Multidimensional Time Model for Probability Cumulative Function Applied to Geometrical Predictions
Applied and Computational Mathematics
Volume 7, Issue 3, June 2018, Pages: 89-93
Received: Nov. 9, 2017;
Accepted: Feb. 8, 2018;
Published: Jul. 5, 2018
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Michael Fundator, Division of Behavioral and Social Sciences and Education, National Academies of Sciences, Engineering, Medicine, Washington, USA
Multidimensional Time Model for Probability Cumulative Function can be reduced to finite-dimensional time model, which can be characterized by Boolean algebra for operations over events and their probabilities and index set for reduction of infinite dimensional time model to finite number of dimensions of time model through application of Boolean prime ideal theorem and Stone duality and can be indexed by an index set considering also the fractal-dimensional time arising from alike supersymmetrical properties of probability through consideration of extension of the classical Stone duality to the category of Boolean spaces, locally compact Hausdorff spaces. The introduction of probabilistical prediction philosophically based on Erdős–Rényi LLN for the prediction through Descartes’ cycles, Gauss methods of trigonometric interpolation and least squares to reduce error in determination of the orbits of planetary bodies, and Farey series continued by sampling on the Sierpinski gasket.
Multidimensional Time Model for Probability Cumulative Function Applied to Geometrical Predictions, Applied and Computational Mathematics.
Vol. 7, No. 3,
2018, pp. 89-93.
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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