Displaying similar documents to “Modelling and identification of voids nucleation and growth effects in porous media plastic flow”

Convergence model of interest rates of CKLS type

Zuzana Zíková, Beáta Stehlíková (2012)

Kybernetika

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This paper deals with convergence model of interest rates, which explains the evolution of interest rate in connection with the adoption of Euro currency. Its dynamics is described by two stochastic differential equations – the domestic and the European short rate. Bond prices are then solutions to partial differential equations. For the special case with constant volatilities closed form solutions for bond prices are known. Substituting its constant volatilities by instantaneous volatilities...

Improved interval DEA models with common weight

Jiasen Sun, Yajun Miao, Jie Wu, Lianbiao Cui, Runyang Zhong (2014)

Kybernetika

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The traditional data envelopment analysis (DEA) model can evaluate the relative efficiencies of a set of decision making units (DMUs) with exact values. But it cannot handle imprecise data. Imprecise data, for example, can be expressed in the form of the interval data or mixtures of interval data and exact data. In order to solve this problem, this study proposes three new interval DEA models from different points of view. Two examples are presented to illustrate and validate these models. ...

Finding target units in FDH model by least-distance measure model

Ali Ebrahimnejad, Reza Shahverdi, Farzad Rezaee Balf, Maryam Hatefi (2013)

Kybernetika

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Recently, some authors used the Least-Distance Measure model in order to obtain the shortest distance between the evaluated Decision Making Unit (DMU) and the strongly efficient production frontier. But, their model is not applicable for situation in which the production possibility set satisfies free disposability property. In this paper, we propose a new approach to this end in FDH model which improves the application potential of the Least-Distance Measure and overcomes the mentioned...

Scaling of model approximation errors and expected entropy distances

Guido F. Montúfar, Johannes Rauh (2014)

Kybernetika

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We compute the expected value of the Kullback-Leibler divergence of various fundamental statistical models with respect to Dirichlet priors. For the uniform prior, the expected divergence of any model containing the uniform distribution is bounded by a constant 1 - γ . For the models that we consider this bound is approached as the cardinality of the sample space tends to infinity, if the model dimension remains relatively small. For Dirichlet priors with reasonable concentration parameters...