Diffusivity in breast malignancies analyzed for b > 1000 s/mm2 at 1 mm in‐plane resolutions: Insight from Gaussian and non‐Gaussian behaviors
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Magnetic Resonance Imaging"
DOI: 10.1002/jmri.27489
Abstract: Diffusion‐weighted imaging (DWI) can improve breast cancer characterizations, but often suffers from low image quality –particularly at informative b > 1000 s/mm2 values. The aim of this study was to evaluate multishot approaches characterizing Gaussian and non‐Gaussian diffusivities… read more here.
Keywords: breast malignancies; breast; non gaussian; mm2 ... See more keywords
Macromolecular background signal and non‐Gaussian metabolite diffusion determined in human brain using ultra‐high diffusion weighting
Sign Up to like & getrecommendations! Published in 2022 at "Magnetic Resonance in Medicine"
DOI: 10.1002/mrm.29367
Abstract: Definition of a macromolecular MR spectrum based on diffusion properties rather than relaxation time differences and characterization of non‐Gaussian diffusion of brain metabolites with strongly diffusion‐weighted MR spectroscopy. read more here.
Keywords: diffusion; non gaussian; macromolecular background; background signal ... See more keywords
An iterative polynomial chaos approach toward stochastic elastostatic structural analysis with non‐Gaussian randomness
Sign Up to like & getrecommendations! Published in 2019 at "International Journal for Numerical Methods in Engineering"
DOI: 10.1002/nme.6086
Abstract: Stochastic analysis of structure with non‐Gaussian material property and loading in the framework of polynomial chaos (PC) is considered. A new approach for the solution of stochastic mechanics problem with random coefficient is presented. The… read more here.
Keywords: analysis; mechanics; method; polynomial chaos ... See more keywords
Cauchy kernel correntropy‐based robust multi‐innovation identification method for the nonlinear exponential autoregressive model in non‐Gaussian environment
Sign Up to like & getrecommendations! Published in 2024 at "International Journal of Robust and Nonlinear Control"
DOI: 10.1002/rnc.7338
Abstract: This paper discusses the identification problem of the nonlinear exponential autoregressive model in the non‐Gaussian noise environment. To suppress the negative influence caused by the non‐Gaussian noise on the accuracy of the identification, this paper… read more here.
Keywords: kernel correntropy; correntropy based; cauchy kernel; multi innovation ... See more keywords
Non-Gaussian Lagrangian Stochastic Model for Wind Field Simulation in the Surface Layer
Sign Up to like & getrecommendations! Published in 2019 at "Advances in Atmospheric Sciences"
DOI: 10.1007/s00376-019-9052-7
Abstract: Wind field simulation in the surface layer is often used to manage natural resources in terms of air quality, gene flow (through pollen drift), and plant disease transmission (spore dispersion). Although Lagrangian stochastic (LS) models… read more here.
Keywords: wind; non gaussian; surface layer; model ... See more keywords
Estimating a non-Gaussian probability density of the rolling motion in irregular beam seas
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Marine Science and Technology"
DOI: 10.1007/s00773-018-0606-7
Abstract: A methodology for predicting the probability density function of roll motion for irregular beam seas was developed in previous research. That work introduced a non-Gaussian probability density function (PDF), which shows a good agreement with… read more here.
Keywords: motion irregular; probability density; non gaussian; irregular beam ... See more keywords
Information-theoretic evaluation of covariate distributions models
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Pharmacokinetics and Pharmacodynamics"
DOI: 10.1007/s10928-025-09968-5
Abstract: Statistical modelling of covariate distributions allows to generate virtual populations or to impute missing values in a covariate dataset. Covariate distributions typically have non-Gaussian margins and show nonlinear correlation structures, which simple descriptions like multivariate… read more here.
Keywords: covariate; divergence; information theoretic; non gaussian ... See more keywords
Extracting Governing Laws from Sample Path Data of Non-Gaussian Stochastic Dynamical Systems
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Statistical Physics"
DOI: 10.1007/s10955-022-02873-y
Abstract: Advances in data science are leading to new progresses in the analysis and understanding of complex dynamics for systems with experimental and observational data. With numerous physical phenomena exhibiting bursting, flights, hopping, and intermittent features,… read more here.
Keywords: stochastic dynamical; dynamical systems; extracting governing; laws sample ... See more keywords
Inverse stochastic resonance in Hodgkin–Huxley neural system driven by Gaussian and non-Gaussian colored noises
Sign Up to like & getrecommendations! Published in 2020 at "Nonlinear Dynamics"
DOI: 10.1007/s11071-020-05492-y
Abstract: Inverse stochastic resonance (ISR) is the phenomenon of the response of neuron to noise, which is opposite to the conventional stochastic resonance. In this paper, the ISR phenomena induced by Gaussian and non-Gaussian colored noises… read more here.
Keywords: phenomenon; isr; stochastic resonance; non gaussian ... See more keywords
Non-Gaussian Methods for Causal Structure Learning
Sign Up to like & getrecommendations! Published in 2018 at "Prevention Science"
DOI: 10.1007/s11121-018-0901-x
Abstract: Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be… read more here.
Keywords: structure learning; non gaussian; causal; causal structure ... See more keywords
Sampling of Bayesian posteriors with a non-Gaussian probabilistic learning on manifolds from a small dataset
Sign Up to like & getrecommendations! Published in 2020 at "Statistics and Computing"
DOI: 10.1007/s11222-020-09954-6
Abstract: This paper tackles the challenge presented by small-data to the task of Bayesian inference. A novel methodology, based on manifold learning and manifold sampling, is proposed for solving this computational statistics problem under the following… read more here.
Keywords: gaussian probabilistic; non gaussian; sampling bayesian; bayesian posteriors ... See more keywords