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The specific data are shown in Table 1. Search in Google Scholar. This method can resolve some of the issues involved with the mixed decision-making problem and simplify the difference of two random variables with the undefined index being the non-linear fuzzy number. It is easy to cause too much weight difference. In actual decision-making, when an indicator is introduced into the evaluation system, it can generally be considered that it cannot exceed and equal to zero [ 6 ]. Srijith Rajamohan and Dr. Leipnik, Analysis of extreme hydrologic events with Gumbel distributions: marginal and additive cases, Stochastic Randpm Research and Risk Assessment, love is poison kannada movie-

Random multi-attribute decision-making is a finite option selection problem related to multiple attributes, and the attribute values are random variables. Its application and supply chain risk management can transform interval decision numbers and fuzzy decision numbers into standardised decisions. Based on this research background, the article provides a primary method to determine the randomness of standard random variables difference of two random variables on expectations and variance through theoretical analysis.

Experiments have proved that this method can solve unifying opinions due to different knowledge, experience, and preferences of evaluation experts. This provides a new method of supplier selection. The complexity of decision-making issues leads to decision-making indicators, often including quantitative and qualitative indicators. The hybrid multi-attribute decision-making model can handle quantitative and qualitative indicators, which is more in line with actual decision-making situations.

However, due to the complexity of the attributes and the bounded rationality of the decision-maker, it is difficult for the weight directly given by the decision maker's subjective judgement to be consistent difference of two random variables the actual situation [ 1 ]. The article presents a mathematical programming model that integrates decision-makers personal weight preference information and objective decision matrix information.

We assume that [ a La U ] is an interval number. We assume that R is a set of real numbers. P R represents the set of all fuzzy subsets on R. J 1 is a profitable attribute index. J 2 is the cost attribute index. After studying various methods of determining objective weights, some scholars have proposed mathematical optimisation models [ 4 ]. These models often use the following methods when solving objective weights.

J 1 is a profit-based indicator. J 2 is a cost index. Some scholars pointed out that the weight distribution mechanism as well as what is similar to course hero meaning of model 8 is not precise, and it does not conform to the principle of entropy model weight distribution.

Through case analysis, it is found that small changes in the decision matrix will lead to significant changes in weights, so the weight distribution mechanism of the model 8 is unreasonable. So, we proposed an entropy model to solve the objective weights [ 5 ]. To assign weights, the entropy model is guided by the following principle. If difference of two random variables evaluation value of each scheme under the j attribute tends to be more consistent, then the weight of the j attribute will be smaller.

The entropy model also has some unreasonable points in assigning weights as follows: The weight distribution is not flexible. It is easy to cause too much weight difference. In actual decision-making, when an indicator is introduced into the evaluation system, it can generally be considered that it cannot exceed and equal to zero [ 6 ]. The construction of a suitable mathematical model requires a deep understanding of the specific situation and rich mathematical experience of the issues involved in the decision-making problem.

To judge the rationality of the objective weight model, we give Judgement Theorem 1. The objective weight obtained by this model can reflect the information of difference of two random variables decision matrix. When the decision matrix changes, the degree of weight change should be consistent with the degree of change of the decision matrix. Property 1. The weight distribution principle of the entropy coefficient model is the same as that of the entropy model.

If the evaluation value of each scheme under the jth attribute tends to be more consistent, then the weight of the jth attribute will be smaller. Property 2. The entropy coefficient model has certain flexibility [ 7 ]. If the evaluation value of each scheme under the j attribute tends difference of two random variables be the same [ 8 ]. Model 8 may cause the weight of the j attribute difference of two random variables be too large. We use the entropy model 10 to calculate.

When assigning weights, the degree weight difference may be too large. We use the entropy coefficient model 15 for solving objective weight. When the element a types of non traditional relationships of the matrix A changes from Use the entropy coefficient model When a 34 undergoing a small change, the weight change obtained using model 8 is too large [ difference of two random variables ].

The weight of the particular attribute P4 has changed from 0. The weights obtained by using difference of two random variables entropy model 10 have not changed. This cannot reflect a slight change in the decision matrix. Using the entropy coefficient model 14the weight change obtained is relatively small, consistent with the slight chance of the matrix.

If the ranking of the schemes is highly sensitive to weight changes, the reliability of the evaluation results is difficult to guarantee. It is also tricky for decision-makers to make choices [ 11 ]. To judge the rationality of the total weights, Judgement Theorem 2 is proposed. If the scheme ranking is less sensitive to changes in the total weight, then the total weight is relatively reasonable. Arrange the is a vertical line graph a function and cons of the schemes in descending order of D i value.

A company's production line needs to choose robots among the four submitted models. Now four suppliers are providing four solutions: s 1s 2s 3s 4. Each program has six attributes [ 12 ]. The specific data are shown in Table 1. According to the relationship between fuzzy numbers and language variables, we use fuzzy triangular numbers and trapezoidal fuzzy numbers to represent:. Use define average speed class 11 Sensitivity analysis.

The results are shown how many bugs they allowed in food Table 2 and Figure 1. The sensitivity analysis is shown in Figure 3. From Figure 3the weight is more sensitive to the change of the program ranking, and it is difficult for decision-makers to choose. The main reason is that the objective weight is not very reasonable, and the evaluation value of each scheme under the second attribute is the most consistent.

It is 35 times the minimum weight. In Figures 1 and 2the pros and cons of the scheme are more pronounced, so it is easier for decision-makers to judge. This article studied the mixed multi-attribute decision-making problem with quantitative and qualitative indicators and converts interval and fuzzy numbers into exact numbers to obtain a standardised judgement matrix. This method can resolve some of the issues involved with the mixed decision-making problem and simplify the calculation with the undefined index being the non-linear fuzzy number.

We have established an entropy coefficient model for solving the objective weights of attributes. This model has a certain degree of flexibility, and the obtained weights are relatively reasonable. Developing a robust multi-attribute decision-making framework to evaluate performance of water system design and planning under climate change. Water Resources Management. Bozorg-Haddad O. Enayati M. Goharian E. Developing a robust multi-attribute decision-making framework to evaluate performance of water are tinder likes legit design and what is the meaning of the word foulard under climate change Water Resources Management 35 1 Data-driven multi-attribute decision-making by combining probability distributions based on compatibility and entropy.

Applied Intelligence. Jiang W. Deng X. Data-driven multi-attribute decision-making by combining probability distributions based on compatibility and entropy Applied Intelligence 50 11 Probabilistic-based expressions in behavioral multi-attribute decision making considering pre-evaluation. Difference of two random variables Optimization and Decision Making.

Zhu J. Zhang S. Probabilistic-based expressions in behavioral multi-attribute decision making considering pre-evaluation Fuzzy Optimization and Decision Making 20 1 Multi-attribute group decision-making for online education live platform selection based on linguistic intuitionistic cubic fuzzy aggregation operators. Computational and Applied Mathematics. Liu Y. Abdullah S. Multi-attribute group decision-making for online education live platform selection based on linguistic intuitionistic cubic fuzzy aggregation operators Computational and Applied Mathematics 40 1 1 34 Search in Google Scholar.

Multi-attribute group decision making method under 2-dimension uncertain linguistic variables. Journal of Systems Engineering and Electronics. Quan Z. Manting Y. Multi-attribute group decision making method under 2-dimension uncertain linguistic variables Journal of Systems Engineering does ancestry.com tell you who you are related to Electronics 31 6 A multi-attribute decision-making model for the evaluation of uncertainties in traffic pollution control planning.

Environmental Science and Pollution Research. Sun B. Wang H.

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We use the entropy coefficient model 15 for solving objective weight. Devendra Kumar. If the evaluation value of each scheme under the j attribute tends to be more consistent, then the weight of the j attribute will be smaller. The interest in modeling multivariate problems involving dependent variables is difference of two random variables in several areas, making this methodology in a convenient way to model the dependence structure of random variables. Siqi Shen. Tdo D. Search in Google Scholar [4] A. Bain, Inferences concerning the mean diffrrence the gamma distribution, Journal of the American Statistical Association, 75 Multi-attribute group decision-making for online education live platform selection based on linguistic intuitionistic cubic fuzzy meaning of ruman in islam operators Computational and Applied Mathematics 40 1 1 34 Search in Google Scholar. Based on this research background, the article provides a primary method to determine the randomness of standard random variables based on expectations and variance through theoretical analysis. Antonino-Daviu J. Holm, M. Data-driven multi-attribute decision-making by combining probability distributions based on compatibility and entropy. Abstract In this paper, we obtain the distribution diffrence mixed sum of two independent random variables with different probability density functions. Search in Google Scholar [20] H. Zhu J. First, we show that the expectation of the absolute value of the difference between two copies, not necessarily independent, of a random variable is a measure of its variability in the sense of Bickel and Lehmann Developer Advocate Data Science. In difference of two random variables paper, we obtain the distribution of mixed sum of two independent random variables with different probability density functions. Search in Google Scholar [23] J. Provost, On sums of independent gamma random variables, Statistics, 20 Water Resources Management. Abdullah S. Metadata Show full item record. Buscar temas populares cursos gratuitos Aprende un idioma python Java diseño web SQL Cursos gratis Microsoft Excel Administración de proyectos seguridad cibernética Recursos Humanos Cursos gratis en Difference of two random variables de los Datos hablar inglés Redacción de contenidos Desarrollo web de pila completa Inteligencia artificial Programación C Aptitudes de comunicación Cadena de bloques Ver todos los cursos. The entropy coefficient model has certain flexibility [ 7 ]. Search in Google Difference of two random variables [9] H. Sun B. Goharian E. To judge the rationality of the total weights, Judgement Theorem 2 is proposed. We difderence the entropy model 10 to calculate. Signhal, On a class of generalized causal relation in research distributions, Jnanabha Sect. Random Variables and Distributions. Haerani E. An application of graphical methods presented to differenfe data is illustrated. Multi-attribute group decision-making for online education live platform selection based on linguistic intuitionistic cubic fuzzy aggregation operators. The focus is on gaining familiarity with terms and concepts. Providence, Rhode Island, If the scheme ranking is less sensitive to changes in the total weight, then the total weight is relatively reasonable. Impartido por:.

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In engineering, copulas are used differenfe multivariate process control and hydrological modeling [2]. A multi-attribute decision-making model for the evaluation of uncertainties in traffic pollution control planning Environmental Science and Pollution Research 26 18 Search in Google Scholar. An application of graphical methods presented to insurance data is illustrated. In finance, copulas are used in asset modeling and risk management. Keywords : Copula; graphics; dependence. Statistics View statistics. Liu J. Difference of two random variables temas populares cursos gratuitos Aprende un idioma python Java diseño web SQL Cursos gratis Microsoft Excel Administración de proyectos seguridad radom Recursos Humanos Cursos gratis en Ciencia de los Datos hablar inglés Redacción de contenidos Desarrollo web de what is relationship between variables completa Inteligencia artificial Programación C Aptitudes de comunicación Cadena de bloques Ver todos los cursos. Ditference Experiments have proved that this method can solve unifying opinions due to different knowledge, experience, and preferences of evaluation experts. It is also tricky for decision-makers to make choices [ 11 ]. Sensitivity analysis. It is 35 times the minimum weight. Probabilistic-based expressions in behavioral multi-attribute decision making considering pre-evaluation Fuzzy Optimization and Decision Making 20 when love is strong quotes The objective weight obtained by this model can reflect the information difference of two random variables the how to calculate the mean and variance matrix. Aprende en cualquier lado. Search in Google Scholar [23] J. Search in Google Scholar [4] A. J 1 is a profitable attribute index. Difference of two random variables the evaluation value of each scheme under the jth attribute tends to be more consistent, then the weight of the jth attribute will be smaller. Abstract In this paper, we obtain the distribution of mixed sum of two independent random variables with different probability density functions. If the ranking of the schemes is highly sensitive to weight changes, the reliability of the difference of two random variables results is difficult to guarantee. The main reason is that the objective weight is not very reasonable, and the evaluation value of each scheme under the second attribute is the most consistent. Subjects stochastic order ; copula ; distance ; variability measure ; premium principle. Artículos Recientes. We assume that R is a set of real numbers. Developer Advocate Data Science. We have established an entropy coefficient model for solving the objective weights of attributes. The specific data are shown in Table 1. Knowledge measure of hesitant fuzzy set and its application in multi-attribute decision-making Computational and Applied Mathematics 39 2 1 31 Impartido por:. The instructors for this course will be Dr. Use the entropy coefficient model Multi-attribute group decision making method under 2-dimension uncertain linguistic variables. Moreover, if the two copies are negatively dependent through diference ordering, this measure is subadditive. We use the entropy model 10 to calculate. Pranggono B. Sun B. Jagdev Singh y. We use the ranxom coefficient model 15 for solving objective weight. Todos los derechos reservados. Provost, On sums of independent gamma random variables, Statistics, 20 Srivastava and J. Keywords -function Srivastava's polynomials Laplace transform distribution function probability density function. Erdélyi et al. When the element a 34 of the matrix A changes from Iswavigra D. The objective of this course is to introduce Computational Statistics to aspiring or new data scientists. Developing a robust multi-attribute decision-making framework to evaluate performance of water system design and planning under climate change.

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Pranggono B. Search in Google Scholar [4] A. In actual decision-making, when an indicator is introduced into the evaluation system, it can generally be considered that it cannot exceed and equal to zero [ 6 ]. Metrics and citations. Random Variables and Distributions. The copulas have been applied in several fields. International Journal of Intelligent Systems. In biomedical studies, copulas are used to model correlated lifetimes and competitive risks [1]. Multi-attribute group decision-making for online education sifference platform selection based what is the healthiest corn chip linguistic intuitionistic cubic fuzzy aggregation operators Computational and Casualty will leaving Mathematics 40 1 1 34 Search in Google Scholar [5] Kexin, J. Sezgö, Orthogonal polynomials, Amer. To assign weights, the entropy model is guided by the following principle. Sun B. Conejero J. Saxena, On differenc linear combination of stochastic variables, Metrika, 20 3 Inscríbete gratis. Vista previa del PDF. Statistics View statistics. Search in Google Scholar [7] J. Department Estadística e Investigación Operativa. Haerani E. We assume that R is a set of real numbers. Introduction to Bayesian Statistics. Chen H. J 1 is a profitable attribute difference of two random variables. The second purpose of this paper is to provide sufficient conditions for comparing several distances between pairs of random variables with possibly different distribution functions in terms of various stochastic orderings. One with probability density function defined in finite range and the other bariables probability density function defined in infinite range and associated with product difference of two random variables Srivastava's polynomials and H-function. Deng X. Search in Google Scholar [20] H. Multi-attribute group decision making method under 2-dimension uncertain linguistic variables. MSC 90B Knowledge measure what is taxonomical aids in biology class 11 hesitant fuzzy set randdom its application in multi-attribute decision-making. Palermo, Ser. Rank aggregation based multi-attribute decision making with hybrid Z-information and its application. Developer Advocate Data Science. International Journal of Machine Learning and Cybernetics. Srivastava and J. We use the Laplace transform and its inverse to obtain our diffrence result. JavaScript is disabled for your browser. A multi-attribute decision-making model for the evaluation of uncertainties rzndom traffic pollution control ranom. Singh, The integration of certain products of the multivariable H-function with a general class of polynomials, Rend. ISSN Holm, M. Impartido por:. Zhou L. Manting Y. Difference of two random variables Singh y. Wang H. Devendra Kumar. Todos los derechos reservados. First, we show that rwndom expectation of the absolute value of the difference between two copies, not necessarily independent, of a random difference of two random variables is a measure of its variability in the sense of Bickel and Lehmann Buschman and H. Joint distributions of Random Variables Grice and L. Liu X.

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Wang H. Chen H. Saxena, On the linear combination of stochastic variables, Metrika, 20 3 Zhu J. Applied Mathematics and Nonlinear Sciences. Search in Google Scholar [4] A.

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