G
Gholamhossein Yari
Researcher at Iran University of Science and Technology
Publications - 52
Citations - 526
Gholamhossein Yari is an academic researcher from Iran University of Science and Technology. The author has contributed to research in topics: Entropy (information theory) & Rényi entropy. The author has an hindex of 11, co-authored 50 publications receiving 444 citations. Previous affiliations of Gholamhossein Yari include Islamic Azad University of Tabriz.
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Some properties of Rényi entropy and Rényi entropy rate
TL;DR: The conditional Renyi entropy is defined and it is shown that the so-called chain rule holds for the Ren Yi entropy and the bound for theRenyi entropy rate is simply the Shannon entropy rate.
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Acceptance single sampling plan with fuzzy parameter
TL;DR: In this article, the acceptance double sampling plan when the fraction of defective items is a fuzzy number is analyzed and the operating characteristic curves of the plan is shown to be like a band having a high and low bounds whose width depends on the ambiguity proportion parameter in the lot when that sample size and acceptance numbers is fixed.
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On weighted interval entropy
F. Misagh,Gholamhossein Yari +1 more
TL;DR: This paper studies the weighted differential information measure for two-sided truncated random variables, a generalization of recent dynamic weighted entropy measures, and obtains its upper and lower bounds.
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Inspection error and its effects on single sampling plans with fuzzy parameters
TL;DR: In this paper, an acceptance single sampling plan with inspection errors when the fraction of defective items is a fuzzy number was designed, and the operating characteristics curve of this plan is like a band having high and low bounds, its width depends on the ambiguity of proportion parameter in the lot when the samples size and acceptance numbers are fixed.
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Maximum Bayesian entropy method for determining ordered weighted averaging operator weights
Gholamhossein Yari,Alireza Chaji +1 more
TL;DR: The maximum Bayesian entropy approach for obtaining the OWA operator weights is proposed and it is assumed, according to previous experiences or from theoretical considerations that a decision maker may have reasons to consider a given prior OWA vector.