The Stein-Dirichlet-Malliavin method [PDF]
ESAIM: Proceedings and Surveys, 2015 The Stein’s method is a popular method used to derive upper-bounds of distances between probability distributions. It can be viewed, in certain of its formulations, as an avatar of the semi-group or of the smart-path method used ...
Decreusefond L.
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Self-supervised MRI denoising: leveraging Stein’s unbiased risk estimator and spatially resolved noise maps [PDF]
Scientific Reports, 2023 Thermal noise caused by the imaged object is an intrinsic limitation in magnetic resonance imaging (MRI), resulting in an impaired clinical value of the acquisitions.
Laura Pfaff +8 more
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Automated Parameter Selection for Accelerated MRI Reconstruction via Low-Rank Modeling of Local k-Space Neighborhoods [PDF]
Zeitschrift für Medizinische Physik, 2023 Purpose: Image quality in accelerated MRI rests on careful selection of various reconstruction parameters. A common yet tedious and error-prone practice is to hand-tune each parameter to attain visually appealing reconstructions.
Efe Ilicak +2 more
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Performance Enhancement of INS and UWB Fusion Positioning Method Based on Two-Level Error Model [PDF]
Sensors, 2023 In GNSS-denied environments, especially when losing measurement sensor data, inertial navigation system (INS) accuracy is critical to the precise positioning of vehicles, and an accurate INS error compensation model is the most effective way to improve ...
Zhonghan Li +4 more
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Approximations of normal distribution by its q-generalizations [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2021 A concept of q-generalization of normal distribution arises in the context of statistical mechanics. In this article, we introduce a q-generalization of normal approximation.
Mongkhon Tuntapthai
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Some recent advances for limit theorems [PDF]
ESAIM: Proceedings and Surveys, 2020 We present some recent developments for limit theorems in probability theory, illustrating the variety of this field of activity. The recent results we discuss range from Stein’s method, as well as for infinitely divisible distributions as applications ...
Arras Benjamin +4 more
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Approximation Results for Sums of Independent Random Variables
Revstat Statistical Journal, 2022 In this article, we consider Poisson and Poisson convoluted geometric approximation to the sums of n independent random variables under moment conditions. We use Stein’s method to derive the approximation results in total variation distance.
Pratima Eknath Kadu
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A non-uniform bound on binomial approximation to the beta binomial cumulative distribution function [PDF]
Songklanakarin Journal of Science and Technology (SJST), 2019 This paper uses Stein’s method and the characterization of beta binomial random variable to determine a non-uniform bound for the distance between the beta binomial cumulative distribution function with parameters n N, 0 and 0 and the ...
Kanint Teerapabolarn, Khunakorn Sae-Jeng
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Interferometric SAR Phase Filtering With SURE-Based Non-Local Method
IEEE Access, 2020 As the phase of the interferometric synthetic aperture radar (InSAR) contains abundant information for many earth observation activities, the interferometric phase denoising is an important step before InSAR processing and application because of its ...
Rui Guo +3 more
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Malliavin–Stein method: a survey of some recent developments
Modern Stochastics: Theory and Applications, 2021 Initiated around the year 2007, the Malliavin–Stein approach to probabilistic approximations combines Stein’s method with infinite-dimensional integration by parts formulae based on the use of Malliavin-type operators. In the last decade, Malliavin–Stein
Ehsan Azmoodeh +2 more
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