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Federico Milano  One of the best experts on this subject based on the ideXlab platform.

data based continuous wind speed models with arbitrary Probability Distribution and autocorrelation
Renewable Energy, 2019CoAuthors: Guðrun Margret Jonsdottir, Federico MilanoAbstract:Abstract The paper presents a systematic method to build dynamic stochastic models from wind speed measurement data. The resulting models fit any Probability Distribution and any autocorrelation that can be approximated through a weighted sum of decaying exponential and/or damped sinusoidal functions. The proposed method is tested by means of realworld wind speed measurement data with sampling rates ranging from seconds to hours. The statistical properties of the wind speed time series and the synthetic stochastic processes generated with the Stochastic Differential Equation (SDE)based models are compared. Results indicate that the proposed method is simple to implement, robust and can accurately capture simultaneously the autocorrelation and Probability Distribution of wind speed measurement data.
Guðrun Margret Jonsdottir  One of the best experts on this subject based on the ideXlab platform.

data based continuous wind speed models with arbitrary Probability Distribution and autocorrelation
Renewable Energy, 2019CoAuthors: Guðrun Margret Jonsdottir, Federico MilanoAbstract:Abstract The paper presents a systematic method to build dynamic stochastic models from wind speed measurement data. The resulting models fit any Probability Distribution and any autocorrelation that can be approximated through a weighted sum of decaying exponential and/or damped sinusoidal functions. The proposed method is tested by means of realworld wind speed measurement data with sampling rates ranging from seconds to hours. The statistical properties of the wind speed time series and the synthetic stochastic processes generated with the Stochastic Differential Equation (SDE)based models are compared. Results indicate that the proposed method is simple to implement, robust and can accurately capture simultaneously the autocorrelation and Probability Distribution of wind speed measurement data.
Kenji Morita  One of the best experts on this subject based on the ideXlab platform.

criticality of the net baryon number Probability Distribution at finite density
Physics Letters B, 2015CoAuthors: Kenji Morita, Bengt Friman, K RedlichAbstract:Abstract We compute the Probability Distribution P ( N ) of the netbaryon number at finite temperature and quarkchemical potential, μ , at a physical value of the pion mass in the quarkmeson model within the functional renormalization group scheme. For μ / T 1 , the model exhibits the chiral crossover transition which belongs to the universality class of the O ( 4 ) spin system in three dimensions. We explore the influence of the chiral crossover transition on the properties of the net baryon number Probability Distribution, P ( N ) . By considering ratios of P ( N ) to the Skellam function, with the same mean and variance, we unravel the characteristic features of the Distribution that are related to O ( 4 ) criticality at the chiral crossover transition. We explore the corresponding ratios for data obtained at RHIC by the STAR Collaboration and discuss their implications. We also examine O ( 4 ) criticality in the context of binomial and negativebinomial Distributions for the net proton number.

net baryon number Probability Distribution near the chiral phase transition
European Physical Journal C, 2014CoAuthors: Kenji Morita, Bengt Friman, Vladimir Skokov, Krzysztof RedlichAbstract:We discuss the properties of the net baryon number Probability Distribution near the chiral phase transition to explore the effect of critical fluctuations. Our studies are performed within Landau theory, where the coefficients of the polynomial potential are parametrized, so as to reproduce the meanfield (MF), the \(Z(2)\), and the \(O(4)\) scaling behaviors of the cumulants of the net baryon number. We show that in the critical region the structure of the Probability Distribution changes, depending on the values of the critical exponents. In the MF approach, as well as in the \(Z(2)\) universality class, the contribution of the singular part of the thermodynamic potential tends to broaden the Distribution. By contrast, in the model with \(O(4)\) scaling, the contribution of the singular part results in a narrower net baryon number Probability Distribution with a wide tail.

net quark number Probability Distribution near the chiral crossover transition
Physical Review C, 2013CoAuthors: Kenji Morita, Bengt Friman, Krzysztof Redlich, Vladimir SkokovAbstract:We investigate properties of the Probability Distribution of the net quark number near the chiral crossover transition in the quarkmeson model. The calculations are performed within the functional renormalization group approach, as well as in the meanfield approximation. We find, that there is a substantial influence of the underlying chiral phase transition on the properties of the Probability Distribution. In particular, for a physical pion mass, the Distribution which includes the effect of mesonic fluctuations, differs considerably from both, the meanfield and Skellam Distributions. The latter is considered as a reference for a noncritical behavior. A characteristic feature of the net quark number Probability Distribution is that, in the vicinity of the chiral crossover transition in the O(4) universality class, it is narrower than the corresponding meanfield and Skellam function. We study the volume dependence of the Probability Distribution, as well as the resulting cumulants, and discuss their approximate scaling properties. PACS numbers: 25.75.Nq, 25.75.Gz, 24.60.k, 12.39.Fe

net baryon number Probability Distribution near the chiral phase transition
arXiv: High Energy Physics  Phenomenology, 2012CoAuthors: Kenji Morita, Bengt Friman, Vladimir Skokov, Krzysztof RedlichAbstract:We discuss properties of the net baryon number Probability Distribution near the chiral phase transition to explore the effect of critical fluctuations. Our studies are performed within Landau theory, where the coefficients of the polynomial potential are parametrized, so as to reproduce the meanfield (MF), the $Z(2)$ and $O(4)$ scaling behaviors of the cumulants of the net baryon number. We show, that in the critical region, the structure of the Probability Distribution changes, dependently on values of the critical exponents. In the MF approach, as well as in the $Z(2)$ universality class, the contribution of the singular part of the thermodynamic potential tends to broaden the Distribution. By contrast, in the model with $O(4)$ scaling, the contribution of the singular part results in a narrower net baryon number Probability Distribution with a wide tail.
Victor M Yakovenko  One of the best experts on this subject based on the ideXlab platform.

Probability Distribution of returns in the heston model with stochastic volatility
arXiv: Statistical Mechanics, 2002CoAuthors: Adrian Dragulescu, Victor M YakovenkoAbstract:We study the Heston model, where the stock price dynamics is governed by a geometrical (multiplicative) Brownian motion with stochastic variance. We solve the corresponding FokkerPlanck equation exactly and, after integrating out the variance, find an analytic formula for the timedependent Probability Distribution of stock price changes (returns). The formula is in excellent agreement with the DowJones index for the time lags from 1 to 250 trading days. For large returns, the Distribution is exponential in logreturns with a timedependent exponent, whereas for small returns it is Gaussian. For time lags longer than the relaxation time of variance, the Probability Distribution can be expressed in a scaling form using a Bessel function. The DowJones data for 19822001 follow the scaling function for seven orders of magnitude.

Probability Distribution of returns in the heston model with stochastic volatility
Computing in Economics and Finance, 2002CoAuthors: Adrian Dragulescu, Victor M YakovenkoAbstract:We study the Heston model, where the stock price dynamics is governed by a geometrical (multiplicative) Brownian motion with stochastic variance. We solve the corresponding FokkerPlanck equation exactly and, after integrating out the variance, find an analytic formula for the timedependent Probability Distribution of stock price changes (returns). The formula is in excellent agreement with the DowJones index for the time lags from 1 to 250 trading days. For large returns, the Distribution is exponential in logreturns with a timedependent exponent, whereas for small returns it is Gaussian. For time lags longer than the relaxation time of variance, the Probability Distribution can be expressed in a scaling form using a Bessel function. The DowJones data for 19822001 follow the scaling function for seven orders of magnitude. (This abstract was borrowed from another version of this item.)
S. Han  One of the best experts on this subject based on the ideXlab platform.

The time scale study of wind speed Probability Distribution
2nd IET Renewable Power Generation Conference (RPG 2013), 2013CoAuthors: Y.q. Liu, J.t. Huang, S. HanAbstract:Across different time scales, Probability Distributions of wind speed are not exactly the same. Annually statistical cycle of wind speed Probability Distribution can not reflect wind variations in different time scales. Based on the fact that wind speed fluctuates with weather patterns, the statistical cycle of Probability Distribution is determined and optimal Probability Distribution model according to the specific time scale are selected by applying power spectrum. The example have illustrated that there are timescale characteristics in wind speed Probability Distribution; wind speed Probability Distribution in any consecutive subperiod is similar to that in total time period; the validity of power spectrum analysis identifying the simulating time scale is confirmed.