Search results for "Characteristic function"

showing 10 items of 26 documents

A taylor series model to evaluate the interelemental effects in X-ray fluorescence analysis, applied to the iron-zirconium-diluent system

1995

A semi-empirical model has been developed to quantify the interelemental effects in X-ray fluorescence analysis. The measured X-ray fluorescence intensity has been expressed as a function of the different fluorescence elements composing the sample. this complex function has become an operative function via a Taylor series development. An explication has been given for the significance of the different terms of the series. These terms respond to mathematical functions known as characteristic functions for each chemical system. A parameter (B) has been defined which makes it possible to quantify the influence of the interelemental effect as a function of the analyte concentration (C) and that…

AnalyteZirconiumSeries (mathematics)Characteristic function (probability theory)ChemistryAnalytical chemistryX-ray fluorescencechemistry.chemical_elementFunction (mathematics)BiochemistryFluorescenceAnalytical Chemistrysymbols.namesakeTaylor seriessymbolsFresenius' Journal of Analytical Chemistry
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SHARING THE BENEFITS OF COOPERATION IN HIGH SEAS FISHERIES: A CHARACTERISTIC FUNCTION GAME APPROACH

1998

Characteristic function (convex analysis)0208 environmental biotechnology02 engineering and technology010501 environmental sciencesEnvironmental Science (miscellaneous)01 natural sciencesShapley value020801 environmental engineeringMicroeconomicsInternational watersModeling and SimulationEconomicsMathematical economics0105 earth and related environmental sciencesNatural Resource Modeling
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Opinion dynamics in coalitional games with transferable utilities

2014

This paper studies opinion dynamics in a large number of homogeneous coalitional games with transferable utilities (TU), where the characteristic function is a continuous-time stochastic process. For each game, which we can see as a “small world”, the players share opinions on how to allocate revenues based on the mean-field interactions with the other small worlds. As a result of such mean-field interactions among small worlds, in each game, a central planner allocates revenues based on the extra reward that a coalition has received up to the current time and the extra reward that the same coalition has received in the other games. The paper also studies the convergence and stability of op…

Characteristic function (convex analysis)Opinion dynamicsStochastic processComputer scienceStability (learning theory)RevenueConvergence (relationship)Mathematical economics53rd IEEE Conference on Decision and Control
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Analysis of neuronal networks in the visual system of the cat using statistical signals

1976

If the input signals of the visual system in the cat are statistical patterns in space and time, a complete system analysis can be carried out. What counts here as a system are the neuronal networks between retina and recording site. In the case of linearity, one obtains the temporal impulse response functions at every point in the receptive field with the aid of correlation methods. The measuring time is about one minute. Some aspects of the procedure are explained in terms of examples. The method of measurement also makes it possible to determine the characteristic function of the system in time and space between different recording sites within the cortex. It is possible to specialize th…

Characteristic function (convex analysis)Systems AnalysisGeneral Computer ScienceModels NeurologicalStatistics as TopicComplex systemLinearityNonlinear systemSimple (abstract algebra)Receptive fieldCatsElectronic engineeringAnimalsVisual PathwaysPoint (geometry)AlgorithmImpulse responseBiotechnologyMathematicsBiological Cybernetics
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Is there an absolutely continuous random variable with equal probability density and cumulative distribution functions in its support? Is it unique? …

2014

This paper inquires about the existence and uniqueness of a univariate continuous random variable for which both cumulative distribution and density functions are equal and asks about the conditions under which a possible extrapolation of the solution to the discrete case is possible. The issue is presented and solved as a problem and allows to obtain a new family of probability distributions. The different approaches followed to reach the solution could also serve to warn about some properties of density and cumulative functions that usually go unnoticed, helping to deepen the understanding of some of the weapons of the mathematical statistician’s arsenal.

Characteristic function (probability theory)Cumulative distribution functionCalculusProbability mass functionProbability distributionApplied mathematicsProbability density functionMoment-generating functionRandom variableLaw of the unconscious statisticianMathematicsInternational Journal of Advanced Statistics and Probability
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On the use of fractional calculus for the probabilistic characterization of random variables

2009

In this paper, the classical problem of the probabilistic characterization of a random variable is re-examined. A random variable is usually described by the probability density function (PDF) or by its Fourier transform, namely the characteristic function (CF). The CF can be further expressed by a Taylor series involving the moments of the random variable. However, in some circumstances, the moments do not exist and the Taylor expansion of the CF is useless. This happens for example in the case of $\alpha$--stable random variables. Here, the problem of representing the CF or the PDF of random variables (r.vs) is examined by introducing fractional calculus. Two very remarkable results are o…

Characteristic function (probability theory)FOS: Physical sciencesAerospace EngineeringMathematics - Statistics TheoryOcean EngineeringProbability density functionComplex order momentStatistics Theory (math.ST)Fractional calculusymbols.namesakeIngenieurwissenschaftenFOS: MathematicsTaylor seriesApplied mathematicsCharacteristic function serieMathematical PhysicsCivil and Structural EngineeringMathematicsGeneralized Taylor serieMechanical EngineeringStatistical and Nonlinear PhysicsProbability and statisticsMathematical Physics (math-ph)Condensed Matter PhysicsFractional calculusFourier transformNuclear Energy and EngineeringPhysics - Data Analysis Statistics and ProbabilitysymbolsFractional calculus; Generalized Taylor series; Complex order moments; Fractional moments; Characteristic function series; Probability density function seriesddc:620Series expansionFractional momentProbability density function seriesSettore ICAR/08 - Scienza Delle CostruzioniRandom variableData Analysis Statistics and Probability (physics.data-an)
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Path integral solution for non-linear system enforced by Poisson White Noise

2008

Abstract In this paper the response in terms of probability density function of non-linear systems under Poisson White Noise is considered. The problem is handled via path integral (PI) solution that may be considered as a step-by-step solution technique in terms of probability density function. First the extension of the PI to the case of Poisson White Noise is derived, then it is shown that at the limit when the time step becomes an infinitesimal quantity the Kolmogorov–Feller (K–F) equation is fully restored enforcing the validity of the approximations made in obtaining the conditional probability appearing in the Chapman Kolmogorov equation (starting point of the PI). Spectral counterpa…

Characteristic function (probability theory)Mechanical EngineeringMathematical analysisFokker-Planck equationAerospace EngineeringConditional probabilityKolmogorov-Feller eqautionOcean EngineeringStatistical and Nonlinear PhysicsProbability density functionWhite noiseCondensed Matter PhysicsPoisson distributionPath Integral Solutionsymbols.namesakeNuclear Energy and EngineeringPath integral formulationsymbolsFokker–Planck equationSettore ICAR/08 - Scienza Delle CostruzioniChapman–Kolmogorov equationCivil and Structural EngineeringMathematicsProbabilistic Engineering Mechanics
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A method for the probabilistic analysis of nonlinear systems

1995

Abstract The probabilistic description of the response of a nonlinear system driven by stochastic processes is usually treated by means of evaluation of statistical moments and cumulants of the response. A different kind of approach, by means of new quantities here called Taylor moments, is proposed. The latter are the coefficients of the Taylor expansion of the probability density function and the moments of the characteristic function too. Dual quantities with respect to the statistical cumulants, here called Taylor cumulants, are also introduced. Along with the basic scheme of the method some illustrative examples are analysed in detail. The examples show that the proposed method is an a…

Characteristic function (probability theory)Stochastic processMechanical EngineeringAerospace EngineeringOcean EngineeringStatistical and Nonlinear PhysicsProbability density functionCondensed Matter Physicssymbols.namesakeNonlinear systemNuclear Energy and EngineeringTaylor seriessymbolsCalculusApplied mathematicsProbabilistic analysis of algorithmsCumulantCivil and Structural EngineeringMathematicsTaylor expansions for the moments of functions of random variables
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Probabilistic response of nonlinear systems under combined normal and Poisson white noise via path integral method

2011

In this paper the response in terms of probability density function of nonlinear systems under combined normal and Poisson white noise is considered. The problem is handled via a Path Integral Solution (PIS) that may be considered as a step-by-step solution technique in terms of probability density function. A nonlinear system under normal white noise, Poissonian white noise and under the superposition of normal and Poisson white noise is performed through PIS. The spectral counterpart of the PIS, ruling the evolution of the characteristic functions is also derived. It is shown that at the limit when the time step becomes an infinitesimal quantity an equation ruling the evolution of the pro…

Characteristic function (probability theory)Stochastic resonanceMechanical EngineeringMathematical analysisShot noiseAerospace EngineeringOcean EngineeringStatistical and Nonlinear PhysicsProbability density functionWhite noiseCondensed Matter PhysicsPoisson distributionsymbols.namesakeNormal white noise Poisonian white noise combined white noisesAdditive white Gaussian noiseNuclear Energy and EngineeringGaussian noisesymbolsSettore ICAR/08 - Scienza Delle CostruzioniCivil and Structural EngineeringMathematics
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Estimating norms inC*-algebras of discrete groups

1976

LetG be a discrete group, letK be a finite subset ofG and let χ K be the characteristic function ofK. Then χ K acts by convolution as a bounded operator onL2(G). We will prove that the norm |||χ K ||| of this operator always satisfies the following estimate: $$|||\chi _{\rm K} |||^2 \leqq k + 2\sqrt {w\left( {k - 1} \right)\left( {k - w} \right)} + \left( {k - 2} \right)\left( {k - w} \right)$$ . Here .

CombinatoricsDiscrete mathematicsCharacteristic function (probability theory)Discrete groupGeneral MathematicsOperator (physics)ConvolutionBounded operatorMathematicsMathematische Annalen
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