The Experts below are selected from a list of 63444 Experts worldwide ranked by ideXlab platform
Mariya Sirotina - One of the best experts on this subject based on the ideXlab platform.
-
Joint Probability density function of modulated synchronous flow interval duration under conditions of fixed dead time
International Conference on Information Technologies and Mathematical Modelling, 2015Co-Authors: Aleksandr Gortsev, Mariya SirotinaAbstract:A modulated synchronous doubly stochastic flow under conditions of a fixed dead time is considered. After each registered event there is a time of fixed duration T (dead time), during which another flow events are inaccessible for observation. When duration of the dead time period finishes, the first happened event creates the dead time period of duration T again and etc. An explicit form of a Probability density function of interval duration between two adjacent events of modulated synchronous doubly stochastic flow under conditions of a fixed dead time is derived. Also an explicit form of a Joint Probability density function for modulated synchronous flow interval duration is obtained. A recurrent conditions for modulated synchronous flow as well as some probabilistic characteristics of the flow are obtained using the formula for a Joint Probability density function.
-
Joint Probability density function of modulated synchronous flow interval duration
International Conference on Information Technologies and Mathematical Modelling, 2014Co-Authors: Aleksandr Gortsev, Mariya SirotinaAbstract:An explicit form of a Probability density function of interval duration between two adjacent events of modulated synchronous doubly stochastic flow is derived. Also an explicit form of a Joint Probability density function for modulated synchronous flow interval duration is obtained. This flow is one of the mathematical models of information flows, which take place in digital networks with integral service. The flow is considered in stationary mode when there are no transition processes. A recurrent conditions for modulated synchronous flow are obtained using the formula for Joint Probability density function.
Bingtuo Guo - One of the best experts on this subject based on the ideXlab platform.
-
multivariate copula based Joint Probability distribution of water supply and demand in irrigation district
Water Resources Management, 2016Co-Authors: Jinping Zhang, Xiaomin Lin, Bingtuo GuoAbstract:Based on the data series of rainfall, reference crop evapotranspiration and irrigation water from 1970 to 2013 in the Luhun irrigation district of China, the multivariate Joint Probability of water supply and demand are constructed with student t-copula function. The results show that student t-copula function can indicate the associated dependence structure amongst these variables well, and the constructed multivariate copula-based Joint Probability distribution reveal the statistical characteristics and occurrence Probability of different combinations of water supply and water demand. Moreover, the trivariate Joint Probability distribution is more reasonable than the bivariate distribution to reflect the water shortage risk, and these Joint distribution values of different combinations of water supply and demand can provide the technological support for water shortage risk evaluation in the irrigation district.
Dritan Siliqi - One of the best experts on this subject based on the ideXlab platform.
-
The method of Joint Probability distribution functions applied to MAD techniques. The centric case.
Acta Crystallographica Section A Foundations of Crystallography, 2001Co-Authors: Carmelo Giacovazzo, Dritan SiliqiAbstract:Traditional probabilistic approaches consider MAD (multiple-wavelength anomalous-dispersion) data as a special MIR (multiple isomorphous replacement) case. The rigorous method of the Joint Probability distribution functions has been applied to solve the phase problem, with the assumption that the anomalous scatterers' substructure is a priori known. The probabilistic approach is able to handle measurement errors: it has been applied to symmetry-restricted phases and provides simple and efficient formulas.
-
the method of Joint Probability distribution functions applied to the one wavelength anomalous scattering oas case
Acta Crystallographica Section A, 2001Co-Authors: Carmelo Giacovazzo, Dritan SiliqiAbstract:The method of the Joint Probability distribution function is applied to the case in which the positions of the anomalous scatterers are fully or partially known. The mathematical technique is able to handle errors both in the model structure of the located anomalous scatterers and in measurements. A criterion for ranking the more accurate phase estimates is given.
-
the Joint Probability distribution function of structure factors with rational indices iv the p1 case
Acta Crystallographica Section A, 1999Co-Authors: Carmelo Giacovazzo, Dritan Siliqi, Cristina FernandezcastanoAbstract:The method of the Joint Probability distribution functions of structure factors has been extended to reflections with rational indices. The most general case, space group P1, has been considered. The positional parameters are the primitive random variables of our probabilistic approach, while the reflection indices are kept fixed. Quite general Joint Probability distributions have been considered from which conditional distributions have been derived: these proved applicable to the accurate estimation of the real and imaginary parts of a structure factor, given prior information on other structure factors. The method is also discussed in relation to the Hilbert-transform techniques.
-
the Probability distribution function of structure factors with non integral indices iii the Joint Probability distribution in the p1 case
Acta Crystallographica Section A, 1999Co-Authors: Carmelo Giacovazzo, Dritan Siliqi, Angela Altomare, Giovanni Luca Cascarano, Rosanna Rizzi, R SpagnaAbstract:The Joint Probability distribution function method has been developed in P1¯ for reflections with rational indices. The positional atomic parameters are considered to be the primitive random variables, uniformly distributed in the interval (0, 1), while the reflection indices are kept fixed. Owing to the rationality of the indices, distributions like P(Fp1, Fp2) are found to be useful for phasing purposes, where p1 and p2 are any pair of vectorial indices. A variety of conditional distributions like P(|Fp1| | |Fp2|), P(|Fp1| |Fp2), P(\varphi_{{\bf p}_1}|\,|F_{{\bf p}_1}|, F_{{\bf p}_2}) are derived, which are able to estimate the modulus and phase of Fp1 given the modulus and/or phase of Fp2. The method has been generalized to handle the Joint Probability distribution of any set of structure factors, i.e. the distributions P(F1, F2,…, Fn+1), P(|F1| |F2,…, Fn+1) and P(\varphi1| |F|1, F2,…, F_{n+1}) have been obtained. Some practical tests prove the efficiency of the method.
-
the Probability distribution function of structure factors with non integral indices iii the Joint Probability distribution in the p1 case
Acta Crystallographica Section A, 1999Co-Authors: Carmelo Giacovazzo, Dritan Siliqi, Angela Altomare, Giovanni Luca Cascarano, Rosanna Rizzi, R SpagnaAbstract:The Joint Probability distribution function method has been developed in P1; for reflections with rational indices. The positional atomic parameters are considered to be the primitive random variables, uniformly distributed in the interval (0, 1), while the reflection indices are kept fixed. Owing to the rationality of the indices, distributions like P(F(p1), F(p2)) are found to be useful for phasing purposes, where p1 and p2 are any pair of vectorial indices. A variety of conditional distributions like P(|F(p1)| | |F(p2)|), P(|F(p1)| |F(p2)), P(varphi(p1)| |F(p1)|, F(p2)) are derived, which are able to estimate the modulus and phase of F(p1) given the modulus and/or phase of F(p2). The method has been generalized to handle the Joint Probability distribution of any set of structure factors, i.e. the distributions P(F(1), F(2),ellipsis, F(n+1)), P(|F(1)| |F(2),ellipsis, F(n+1)) and P(varphi(1)| |F|(1), F(2),ellipsis, F(n+1)) have been obtained. Some practical tests prove the efficiency of the method.
Xiaosong Tang - One of the best experts on this subject based on the ideXlab platform.
-
copula based Joint Probability function for pga and cav a case study from taiwan
Earthquake Engineering & Structural Dynamics, 2016Co-Authors: Xiaosong Tang, Juipin Wang, Hao KuochenAbstract:Summary This study aims to develop a Joint Probability function of peak ground acceleration (PGA) and cumulative absolute velocity (CAV) for the strong ground motion data from Taiwan. First, a total of 40,385 earthquake time histories are collected from the Taiwan Strong Motion Instrumentation Program. Then, the copula approach is introduced and applied to model the Joint Probability distribution of PGA and CAV. Finally, the correlation results using the PGA-CAV empirical data and the normalized residuals are compared. The results indicate that there exists a strong positive correlation between PGA and CAV. For both the PGA and CAV empirical data and the normalized residuals, the multivariate lognormal distribution composed of two lognormal marginal distributions and the Gaussian copula provides adequate characterization of the PGA-CAV Joint distribution observed in Taiwan. This finding demonstrates the validity of the conventional two-step approach for developing empirical ground motion prediction equations (GMPEs) of multiple ground motion parameters from the copula viewpoint. Copyright © 2016 John Wiley & Sons, Ltd.
Aleksandr Gortsev - One of the best experts on this subject based on the ideXlab platform.
-
Joint Probability density function of modulated synchronous flow interval duration under conditions of fixed dead time
International Conference on Information Technologies and Mathematical Modelling, 2015Co-Authors: Aleksandr Gortsev, Mariya SirotinaAbstract:A modulated synchronous doubly stochastic flow under conditions of a fixed dead time is considered. After each registered event there is a time of fixed duration T (dead time), during which another flow events are inaccessible for observation. When duration of the dead time period finishes, the first happened event creates the dead time period of duration T again and etc. An explicit form of a Probability density function of interval duration between two adjacent events of modulated synchronous doubly stochastic flow under conditions of a fixed dead time is derived. Also an explicit form of a Joint Probability density function for modulated synchronous flow interval duration is obtained. A recurrent conditions for modulated synchronous flow as well as some probabilistic characteristics of the flow are obtained using the formula for a Joint Probability density function.
-
Joint Probability density function of modulated synchronous flow interval duration
International Conference on Information Technologies and Mathematical Modelling, 2014Co-Authors: Aleksandr Gortsev, Mariya SirotinaAbstract:An explicit form of a Probability density function of interval duration between two adjacent events of modulated synchronous doubly stochastic flow is derived. Also an explicit form of a Joint Probability density function for modulated synchronous flow interval duration is obtained. This flow is one of the mathematical models of information flows, which take place in digital networks with integral service. The flow is considered in stationary mode when there are no transition processes. A recurrent conditions for modulated synchronous flow are obtained using the formula for Joint Probability density function.
