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Linear rigidity of stationary stochastic processes [PDF]
Ergodic Theory and Dynamical Systems, 2017 We consider stationary stochastic processes $\{X_{n}:n\in \mathbb{Z}\}$ such that $X_{0}$ lies in the closed linear span of $\{X_{n}:n\neq 0\}$; following Ghosh and Peres, we call such processes linearly rigid. Using a criterion of Kolmogorov, we show that it suffices, for a stationary stochastic process to be linearly rigid, that the spectral density ...
Alexey Bufetov +2 more
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Most Effective Sampling Scheme for Prediction of Stationary Stochastic Processes [PDF]
Complexity, 2022 The problem of finding optimal sampling schemes has been resolved in two models. The novelty of this study lies in its cost efficiency, specifically, for the applied problems with expensive sampling process.
Mohammad Mehdi Saber +4 more
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The stationary behaviour of fluid limits of reversible processes is concentrated on stationary points [PDF]
Networks and Heterogeneous Media, 2013 Assume that a stochastic process can be approximated, when some scale parameter gets large, by a fluid limit (also called 'mean field limit', or 'hydrodynamic limit').
Jean-Yves Le Boudec
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On Markovian cocycle perturbations in classical and quantum probability [PDF]
International Journal of Mathematics and Mathematical Sciences, 2003 We introduce Markovian cocycle perturbations of the groups of transformations associated with classical and quantum stochastic processes with stationary increments, which are characterized by a localization of the perturbation to the algebra of events ...
G. G. Amosov
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The 𝑀-Wright Function in Time-Fractional Diffusion Processes: A Tutorial Survey
International Journal of Differential Equations, 2010 In the present review we survey the properties of a transcendental function of the Wright type, nowadays known as 𝑀-Wright function, entering as a probability density in a relevant class of self-similar stochastic processes that we generally refer to as ...
Francesco Mainardi +2 more
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Stochastically modeling multiscale stationary biological processes
PLOS ONE, 2019 Large scale biological responses are inherently uncertain, in part as a consequence of noisy systems that do not respond deterministically to perturbations and measurement errors inherent to technological limitations. As a result, they are computationally difficult to model and current approaches are notoriously slow and computationally intensive ...
Michael A. Rowland +3 more
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MARTINGALE APPROXIMATION OF NON-STATIONARY STOCHASTIC PROCESSES [PDF]
Stochastics and Dynamics, 2006 We generalise the martingale-coboundary representation of discrete time stochastic processes to the non-stationary case and to random variables in Orlicz spaces. Related limit theorems (CLT, invariance principle, log–log law, probabilities of large deviations) are studied.
Dalibor Volný
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Entropy, 2016 In this paper, we revisit the notion of the “minus logarithm of stationary probability” as a generalized potential in nonequilibrium systems and attempt to illustrate its central role in an axiomatic approach to stochastic nonequilibrium thermodynamics ...
Lowell F. Thompson, Hong Qian
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Introduction to Neutrosophic Stochastic Processes [PDF]
Neutrosophic Sets and Systems, 2023 In this article, the definition of literal neutrosophic stochastic processes is presented for the first time in the form 𝒩𝑡 = 𝜉𝑡 + 𝜂𝑡𝐼 ;𝐼 2 = 𝐼 where both {𝜉(𝑡),𝑡 ∈ 𝑇} and {𝜂(𝑡),𝑡 ∈ 𝑇} are classical real valued stochastic processes.
Mohamed Bisher Zeina, Yasin Karmouta
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Some Convergence Theorems for Stationary Stochastic Processes [PDF]
The Annals of Mathematical Statistics, 1959 Tatsuo Kawata
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