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Transport equation driven by a stochastic measure
Modern Stochastics: Theory and Applications, 2023 The stochastic transport equation is considered where the randomness is given by a symmetric integral with respect to a stochastic measure. For a stochastic measure, only σ-additivity in probability and continuity of paths is assumed.
Vadym Radchenko
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Asymptotic properties of the parabolic equation driven by stochastic measure
Modern Stochastics: Theory and Applications, 2022 A stochastic parabolic equation on $[0,T]\times \mathbb{R}$ driven by a general stochastic measure, for which we assume only σ-additivity in probability, is considered. The asymptotic behavior of its solution as $t\to \infty $ is studied.
Boris Manikin
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Averaging principle for the one-dimensional parabolic equation driven by stochastic measure
Modern Stochastics: Theory and Applications, 2022 A stochastic parabolic equation on $[0,T]\times \mathbb{R}$ driven by a general stochastic measure is considered. The averaging principle for the equation is established. The convergence rate is compared with other results on related topics.
Boris Manikin
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The Burgers equation driven by a stochastic measure
Modern Stochastics: Theory and Applications, 2023 The class of one-dimensional equations driven by a stochastic measure μ is studied. For μ only σ-additivity in probability is assumed. This class of equations includes the Burgers equation and the heat equation.
Vadym Radchenko
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Stochastic measures of arbitrage [PDF]
Top, 2002 Empirical research has provided evidence supporting the existence of arbitrage opportunities in real financial markets although market imperfections are often the main reason to explain these empirical deviations. Consequently, recent literature has turned the attention to imperfect markets in order to extend the most significant results on asset ...
Balbás, Alejandro +1 more
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Iterated stochastic measurements [PDF]
Journal of Physics A: Mathematical and Theoretical, 2012 We describe a measurement device principle based on discrete iterations of Bayesian updating of system state probability distributions. Although purely classical by nature, these measurements are accompanied with a progressive collapse of the system state probability distribution during each complete system measurement.
Bauer, Michel +2 more
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Coarse-Graining of Observables
Quantum Reports, 2022 We first define the coarse-graining of probability measures in terms of stochastic kernels. We define when a probability measure is part of another probability measure and say that two probability measures coexist if they are both parts of a single ...
Stan Gudder
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Measurable Stochastics for Brane Calculus [PDF]
Electronic Proceedings in Theoretical Computer Science, 2010 In Proceedings MeCBIC 2010, arXiv:1011 ...
Giorgio Bacci, Marino Miculan
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A maximum flow network interdiction model in fuzzy stochastic hybrid uncertainty environments [PDF]
Yugoslav Journal of Operations Research, 2023 Uncertainty is an inherent characteristic of a decision-making process. Occasionally, historical data may be insufficient to accurately estimate the probability distribution suitable for an unknown variable.
Bavandi Salim, Bigdeli Hamid
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Weighted survival functional entropy and its properties
Open Physics, 2023 The weighted generalized cumulative residual entropy is a recently defined dispersion measure. This article introduces a new uncertainty measure as a generalization of the weighted generalized cumulative residual entropy, called it the weighted ...
Alomani Ghadah, Kayid Mohamed
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