Search results for "Convergence of measures"

showing 3 items of 3 documents

Convergence Theorems for Varying Measures Under Convexity Conditions and Applications

2022

AbstractIn this paper, convergence theorems involving convex inequalities of Copson’s type (less restrictive than monotonicity assumptions) are given for varying measures, when imposing convexity conditions on the integrable functions or on the measures. Consequently, a continuous dependence result for a wide class of differential equations with many interesting applications, namely measure differential equations (including Stieltjes differential equations, generalized differential problems, impulsive differential equations with finitely or countably many impulses and also dynamic equations on time scales) is provided.

Convergence of measuresconvex inequalitymeasure differential equationsSettore MAT/05 - Analisi MatematicaGeneral Mathematicscontinuous dependence
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Convergence for varying measures in the topological case

2023

In this paper convergence theorems for sequences of scalar, vector and multivalued Pettis integrable functions on a topological measure space are proved for varying measures vaguely convergent.

Mathematics - Functional Analysis28B05Primary 28B20 Secondary 26E25 26A39 28B05 46G10 54C60 54C6526A39setwise convergence vaguely convergence weak convergence of measures locally compact Hausdorff space Vitali's TheoremSettore MAT/05 - Analisi Matematica54C60FOS: MathematicsPrimary 28B20Secondary 26E2554C65Functional Analysis (math.FA)46G10
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Convergence of Measures

2020

One focus of probability theory is distributions that are the result of an interplay of a large number of random impacts. Often a useful approximation can be obtained by taking a limit of such distributions, for example, a limit where the number of impacts goes to infinity. With the Poisson distribution, we have encountered such a limit distribution that occurs as the number of very rare events when the number of possibilities goes to infinity (see Theorem 3.7). In many cases, it is necessary to rescale the original distributions in order to capture the behavior of the essential fluctuations, e.g., in the central limit theorem. While these theorems work with real random variables, we will a…

symbols.namesakeProbability theoryWeak convergencesymbolsLimit (mathematics)Statistical physicsPoisson distributionConvergence of measuresRandom variableBrownian motionMathematicsCentral limit theorem
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