The (a, q) Data Modeling in Probabilistic Reasoning
Science Innovation
Volume 2, Issue 4, August 2014, Pages: 43-62
Received: Oct. 8, 2014; Accepted: Oct. 23, 2014; Published: Oct. 30, 2014
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Author
Richard Douglas, Kazan Federal University, 420008, Tatarstan Republic, the Russian Federation
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Abstract
This article considers a critical and experimental approach on the attributive and qualitative AI data modeling and data retrieval in computational probabilistic reasoning. The mathematical correlation of X≈Y in the d=dx/dy differentiations and its point based locations and matrix based predictions in Markov Models, Rete’s Algorithms and Bayesian fields, with the further development of non-linear ‘human-type’ reasoning in AI. The new approach in the ternary system transition (decimal↔binary) of Brusentsov-Bergman principle by its bound allocation in the ‘mirror-based’ system in tn-1… tn+1 powers, and hereon considers its further data retrieval for suitable matching and translation of probabilistic data differentiation. The causation/probability matrix of this paper regards not only bound/free variable in x1,x2,x3, xn variables, but discovers and explains its further subsets in anXqn formula, where the supposition of d=X/Y regarded not as a mathematical placement of the variable X, but as its attributive (a) and qualitative (q) allocation in a certain value/relevance cell of the Probability Triangle of the ternary system. From where the automated differentiation retrieves only the most relevant/objective anXqn data cell, not the closest by the pre-set context, making the AI selections more assertive and preference based than linear.
Keywords
Probability, Reasoning, Computational Logic, Abstraction Modeling, Probabilistic Reasoning, AI Reasoning, Automated Differentiations, Probability Calculus
To cite this article
Richard Douglas, The (a, q) Data Modeling in Probabilistic Reasoning, Science Innovation. Vol. 2, No. 4, 2014, pp. 43-62. doi: 10.11648/j.si.20140204.12
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