Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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We present an algorithm containing the first step towards solving one of the Fundamental Problems of Non-parametric Mathematical Statistics: determining the distribution of an unknown unimodal continuous population from which we have a large random sample of discrete observations. We will present the “algorithm of non-fitting” with the help of which, by using the so-called relative increment functions as auxiliary functions, one can eliminate a large class of classical continuous unimodal distributions which our population does not belong to. In the remaining class of unimodal and smooth distributions one can approximate the distribution of the population in question. The algorithm is illustrated in three numerical examples.
dr._zoltan_istvan_szabo. 1970. \u201cAlgorithm for Finding the Proper Continuous Distribution Function to a Unimodal Empirical Distribution Function\u201d. Unknown Journal GJSFR Volume 10 (GJSFR Volume 10 Issue 5): .
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The methods for personal identification and authentication are no exception.
The methods for personal identification and authentication are no exception.
Total Score: 106
Country: Unknown
Subject: Uncategorized
Authors: Dr. Zoltan Istvan Szabo (PhD/Dr. count: 1)
View Count (all-time): 54
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Publish Date: 1970 01, Thu
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Neural Networks and Rules-based Systems used to Find Rational and
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We present an algorithm containing the first step towards solving one of the Fundamental Problems of Non-parametric Mathematical Statistics: determining the distribution of an unknown unimodal continuous population from which we have a large random sample of discrete observations. We will present the “algorithm of non-fitting” with the help of which, by using the so-called relative increment functions as auxiliary functions, one can eliminate a large class of classical continuous unimodal distributions which our population does not belong to. In the remaining class of unimodal and smooth distributions one can approximate the distribution of the population in question. The algorithm is illustrated in three numerical examples.
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