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The (a, q) data modeling in probabilistic reasoning

机译:概率推理中的(a,q)数据建模

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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 t~(n-1)...t~(n+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,x~n variables, but discovers and explains its further subsets in a~nXq~n 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 a~nXq~n data cell, not the closest by the pre-set context, making the AI selections more assertive and preference based than linear.
机译:本文考虑了一种在计算概率推理中定性和定性AI数据建模和数据检索的关键和实验方法。 d = dx / dy微分中X≈Y的数学相关性及其在马尔可夫模型,Rete算法和贝叶斯领域中基于点的位置和基于矩阵的预测,以及人工智能中非线性“人型”推理的进一步发展。 Brusentsov-Bergman原理的三元系统转换(十进制-二进制)中的新方法,即在t〜(n-1)... t〜(n + 1)中的“基于镜像”系统中的绑定分配功能,因此考虑将其进一步的数据检索用于概率数据区分的适当匹配和转换。本文的因果关系/概率矩阵不仅考虑x1,x2,x3,x〜n变量中的有界/自由变量,而且在a〜nXq〜n公式中发现并解释了它的其他子集,其中d = X / Y并不是变量X的数学位置,而是变量在三元系统的概率三角形的某个值/相关单元中的属性(a)和质量(q)分配。自动微分仅从那里检索最相关/客观的a_nXq_n数据单元,而不从预设上下文中获取最接近的/客观的a-nXq_n数据单元,从而使AI选择比线性选择更具主见性和偏好性。

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