The Experts below are selected from a list of 68148 Experts worldwide ranked by ideXlab platform
Guedes C Soares - One of the best experts on this subject based on the ideXlab platform.
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trivariate Maximum Entropy distribution of significant wave height wind speed and relative direction
Renewable Energy, 2015Co-Authors: Sheng Dong, Xue Li, Guedes C SoaresAbstract:A trivariate Maximum Entropy distribution of significant wave height, wind speed and the relative direction is proposed here. In this joint distribution, all the marginal variables follow modified Maximum Entropy distributions, and they are combined by a correlation coefficient matrix based on the Nataf transformation. The methods of single extreme factors and of conditional probability are presented for the joint design of trivariate random variables. The corresponding sampling data about significant wave heights, wind speeds and the relative directions from a location in the North Atlantic is applied for statistical analysis, and the results show that the trivariate Maximum Entropy distribution is sufficiently good to fit the data, and method of conditional probability can reduce the design values efficiently.
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parameter estimation of the Maximum Entropy distribution of significant wave height
Journal of Coastal Research, 2013Co-Authors: Sheng Dong, Shanshan Tao, Shuhe Lei, Guedes C SoaresAbstract:Dong, S.; Tao, S.; Lei, S., and Guedes Soares, C., 2013. Parameter estimation of the Maximum Entropy distribution of significant wave height. Journal of Coastal Research, 29(3), 597–604. Coconut Creek (Florida), ISSN 0749-0208. This paper compares the estimation of the four parameters of the Maximum Entropy distribution by different methods and applies them in two test cases with significantly different characteristics of variability. The moment method and the Maximum likelihood method for the Maximum Entropy distribution with four parameters are formulated in the paper. These methods are compared with the moment method for the Maximum Entropy distribution with three parameters and an empirical curve-fitting method, both of which have been used earlier. These four estimation methods are applied to two test cases. One consists of hindcast wave heights at Weizhoudao hydrological station in the northern area of the South China Sea, which is subject to typhoon type of events. The other data set is hindcast wave heights at a location in the North Atlantic Ocean, which is subject to frequent storm weather. The Maximum likelihood and the empirical methods appear to provide the most consistent results.
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bivariate Maximum Entropy distribution of significant wave height and peak period
Ocean Engineering, 2013Co-Authors: Sheng Dong, Nannan Wang, Wei Liu, Guedes C SoaresAbstract:Abstract A bivariate Maximum Entropy distribution of significant wave heights and the corresponding peak periods is proposed. Two approaches are used to construct the bivariate Maximum Entropy distribution, the first of which is to adjust equivalent normal bivariate distributions to data transformed from the univariate Maximum Entropy distributions of wave height and period. The other method is the conditional approach of adjusting one Maximum Entropy marginal distribution to wave height and several log-normal distributions to the wave periods conditional on the wave height. The bivariate Maximum Entropy distribution is compared with other bivariate approaches using data from one location in the North Atlantic and a simulated sample by Monte Carlo method.
Sheng Dong - One of the best experts on this subject based on the ideXlab platform.
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trivariate Maximum Entropy distribution of significant wave height wind speed and relative direction
Renewable Energy, 2015Co-Authors: Sheng Dong, Xue Li, Guedes C SoaresAbstract:A trivariate Maximum Entropy distribution of significant wave height, wind speed and the relative direction is proposed here. In this joint distribution, all the marginal variables follow modified Maximum Entropy distributions, and they are combined by a correlation coefficient matrix based on the Nataf transformation. The methods of single extreme factors and of conditional probability are presented for the joint design of trivariate random variables. The corresponding sampling data about significant wave heights, wind speeds and the relative directions from a location in the North Atlantic is applied for statistical analysis, and the results show that the trivariate Maximum Entropy distribution is sufficiently good to fit the data, and method of conditional probability can reduce the design values efficiently.
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parameter estimation of the Maximum Entropy distribution of significant wave height
Journal of Coastal Research, 2013Co-Authors: Sheng Dong, Shanshan Tao, Shuhe Lei, Guedes C SoaresAbstract:Dong, S.; Tao, S.; Lei, S., and Guedes Soares, C., 2013. Parameter estimation of the Maximum Entropy distribution of significant wave height. Journal of Coastal Research, 29(3), 597–604. Coconut Creek (Florida), ISSN 0749-0208. This paper compares the estimation of the four parameters of the Maximum Entropy distribution by different methods and applies them in two test cases with significantly different characteristics of variability. The moment method and the Maximum likelihood method for the Maximum Entropy distribution with four parameters are formulated in the paper. These methods are compared with the moment method for the Maximum Entropy distribution with three parameters and an empirical curve-fitting method, both of which have been used earlier. These four estimation methods are applied to two test cases. One consists of hindcast wave heights at Weizhoudao hydrological station in the northern area of the South China Sea, which is subject to typhoon type of events. The other data set is hindcast wave heights at a location in the North Atlantic Ocean, which is subject to frequent storm weather. The Maximum likelihood and the empirical methods appear to provide the most consistent results.
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bivariate Maximum Entropy distribution of significant wave height and peak period
Ocean Engineering, 2013Co-Authors: Sheng Dong, Nannan Wang, Wei Liu, Guedes C SoaresAbstract:Abstract A bivariate Maximum Entropy distribution of significant wave heights and the corresponding peak periods is proposed. Two approaches are used to construct the bivariate Maximum Entropy distribution, the first of which is to adjust equivalent normal bivariate distributions to data transformed from the univariate Maximum Entropy distributions of wave height and period. The other method is the conditional approach of adjusting one Maximum Entropy marginal distribution to wave height and several log-normal distributions to the wave periods conditional on the wave height. The bivariate Maximum Entropy distribution is compared with other bivariate approaches using data from one location in the North Atlantic and a simulated sample by Monte Carlo method.
Gianluigi Pillonetto - One of the best experts on this subject based on the ideXlab platform.
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Maximum Entropy properties of discrete time first order stable spline kernel
Automatica, 2016Co-Authors: Tianshi Chen, Tohid Ardeshiri, Francesca Paola Carli, Alessandro Chiuso, Lennart Ljung, Gianluigi PillonettoAbstract:The first order stable spline (SS-1) kernel (also known as the tuned-correlated (TC) kernel) is used extensively in regularized system identification, where the impulse response is modeled as a zero-mean Gaussian process whose covariance function is given by well designed and tuned kernels. In this paper, we discuss the Maximum Entropy properties of this kernel. In particular, we formulate the exact Maximum Entropy problem solved by the SS-1 kernel without Gaussian and uniform sampling assumptions. Under general sampling assumption, we also derive the special structure of the SS-1 kernel (e.g. its tridiagonal inverse and factorization have closed form expression), also giving to it a Maximum Entropy covariance completion interpretation.
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Maximum Entropy properties of discrete time first order stable spline kernel
arXiv: Systems and Control, 2015Co-Authors: Tianshi Chen, Tohid Ardeshiri, Francesca Paola Carli, Alessandro Chiuso, Lennart Ljung, Gianluigi PillonettoAbstract:The first order stable spline (SS-1) kernel is used extensively in regularized system identification. In particular, the stable spline estimator models the impulse response as a zero-mean Gaussian process whose covariance is given by the SS-1 kernel. In this paper, we discuss the Maximum Entropy properties of this prior. In particular, we formulate the exact Maximum Entropy problem solved by the SS-1 kernel without Gaussian and uniform sampling assumptions. Under general sampling schemes, we also explicitly derive the special structure underlying the SS-1 kernel (e.g. characterizing the tridiagonal nature of its inverse), also giving to it a Maximum Entropy covariance completion interpretation. Along the way similar Maximum Entropy properties of the Wiener kernel are also given.
Tianshi Chen - One of the best experts on this subject based on the ideXlab platform.
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Maximum Entropy properties of discrete time first order stable spline kernel
Automatica, 2016Co-Authors: Tianshi Chen, Tohid Ardeshiri, Francesca Paola Carli, Alessandro Chiuso, Lennart Ljung, Gianluigi PillonettoAbstract:The first order stable spline (SS-1) kernel (also known as the tuned-correlated (TC) kernel) is used extensively in regularized system identification, where the impulse response is modeled as a zero-mean Gaussian process whose covariance function is given by well designed and tuned kernels. In this paper, we discuss the Maximum Entropy properties of this kernel. In particular, we formulate the exact Maximum Entropy problem solved by the SS-1 kernel without Gaussian and uniform sampling assumptions. Under general sampling assumption, we also derive the special structure of the SS-1 kernel (e.g. its tridiagonal inverse and factorization have closed form expression), also giving to it a Maximum Entropy covariance completion interpretation.
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Maximum Entropy properties of discrete time first order stable spline kernel
arXiv: Systems and Control, 2015Co-Authors: Tianshi Chen, Tohid Ardeshiri, Francesca Paola Carli, Alessandro Chiuso, Lennart Ljung, Gianluigi PillonettoAbstract:The first order stable spline (SS-1) kernel is used extensively in regularized system identification. In particular, the stable spline estimator models the impulse response as a zero-mean Gaussian process whose covariance is given by the SS-1 kernel. In this paper, we discuss the Maximum Entropy properties of this prior. In particular, we formulate the exact Maximum Entropy problem solved by the SS-1 kernel without Gaussian and uniform sampling assumptions. Under general sampling schemes, we also explicitly derive the special structure underlying the SS-1 kernel (e.g. characterizing the tridiagonal nature of its inverse), also giving to it a Maximum Entropy covariance completion interpretation. Along the way similar Maximum Entropy properties of the Wiener kernel are also given.
Lennart Ljung - One of the best experts on this subject based on the ideXlab platform.
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Maximum Entropy properties of discrete time first order stable spline kernel
Automatica, 2016Co-Authors: Tianshi Chen, Tohid Ardeshiri, Francesca Paola Carli, Alessandro Chiuso, Lennart Ljung, Gianluigi PillonettoAbstract:The first order stable spline (SS-1) kernel (also known as the tuned-correlated (TC) kernel) is used extensively in regularized system identification, where the impulse response is modeled as a zero-mean Gaussian process whose covariance function is given by well designed and tuned kernels. In this paper, we discuss the Maximum Entropy properties of this kernel. In particular, we formulate the exact Maximum Entropy problem solved by the SS-1 kernel without Gaussian and uniform sampling assumptions. Under general sampling assumption, we also derive the special structure of the SS-1 kernel (e.g. its tridiagonal inverse and factorization have closed form expression), also giving to it a Maximum Entropy covariance completion interpretation.
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Maximum Entropy properties of discrete time first order stable spline kernel
arXiv: Systems and Control, 2015Co-Authors: Tianshi Chen, Tohid Ardeshiri, Francesca Paola Carli, Alessandro Chiuso, Lennart Ljung, Gianluigi PillonettoAbstract:The first order stable spline (SS-1) kernel is used extensively in regularized system identification. In particular, the stable spline estimator models the impulse response as a zero-mean Gaussian process whose covariance is given by the SS-1 kernel. In this paper, we discuss the Maximum Entropy properties of this prior. In particular, we formulate the exact Maximum Entropy problem solved by the SS-1 kernel without Gaussian and uniform sampling assumptions. Under general sampling schemes, we also explicitly derive the special structure underlying the SS-1 kernel (e.g. characterizing the tridiagonal nature of its inverse), also giving to it a Maximum Entropy covariance completion interpretation. Along the way similar Maximum Entropy properties of the Wiener kernel are also given.
