نتایج جستجو برای: complex interval vector
تعداد نتایج: 1154280 فیلتر نتایج به سال:
Based on a given Bayesian model of multivariate normal with known variance matrix we will find an empirical Bayes confidence interval for the mean vector components which have normal distribution. We will find this empirical Bayes confidence interval as a conditional form on ancillary statistic. In both cases (i.e. conditional and unconditional empirical Bayes confidence interval), the empiri...
بر اساس پخش و پراکندگی چینه شناسی روزنبران پلانکتونیک شناسایی شده از برش مورد پژوهش 9 بیوزون شناسایی و معرفی شد که منطبق با بایوزون های استاندارد جهانی در قلمرو تتیس بوده و به ترتیب ازپایین به بالا شامل : بیوزون 1)dicarinella concavata interval zone (سانتونین پسین) بیوزون 2) dicarinella asymetrica total range zone ( کامپانین میانی) بیوزون 3) globotruncanita elevata interval zone ( کامپانین پ...
Support vector machines (classification and regression) are powerful machine learning techniques for crisp data. In this paper, the problem is considered for interval data. Two methods to deal with the problem using support vector regression are proposed and two new methods for evaluating performance for estimating prediction interval are presented as well.
pseudo ricci symmetric real hypersurfaces of a complex projective space are classified and it is proved that there are no pseudo ricci symmetric real hypersurfaces of the complex projective space cpn for which the vector field ξ from the almost contact metric structure (φ, ξ, η, g) is a principal curvature vector field.
this work is presented in five parts. in the first part preparation of the starting complex [pt(c^n)cl(dmso)], 1, in which c^n = n(1),c(2?)-chelated, deprotonated 2-phenylpyridine, and dmso = dimethylsulfoxide, and its reaction with 1 equiv of the biphosphine ligands bis(diphenylphosphino)amine, dppa, or bis(diphenylphosphino)methane, dppm, to give the complex [pt(c^n)cl(dppa)], 2, or [pt(c^n)c...
In this work, we propose an approach for computing the compromised solution of an LR fuzzy linear system by using of a ranking function when the coefficient matrix is a crisp mn matrix. To do this, we use expected interval to find an LR fuzzy vector, X , such that the vector (AX ) has the least distance from (b) in 1 norm and the 1 cut of X satisfies the crisp linear system AX = b ...
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