Forecasting confirmed cases of Corona patients in India using regression and Gaussian analysis

2021 ◽  
Author(s):  
Dhyan Chandra Yadav ◽  
Saurabh Pal
Keyword(s):  
Gaussian Analysis

scholarly journals Incremental Non-Gaussian Analysis on Multivariate EEG Signal Data

2012 ◽  
Vol E95.D (12) ◽  
pp. 3010-3016 ◽  
Author(s):  
Kam Swee NG ◽  
Hyung-Jeong YANG ◽  
Soo-Hyung KIM ◽  
Sun-Hee KIM
Keyword(s):  
Eeg Signal ◽  
Gaussian Analysis ◽  
Non Gaussian

scholarly journals Non-Gaussian Analysis of Diffusion Weighted Imaging in Head and Neck at 3T: A Pilot Study in Patients with Nasopharyngeal Carcinoma

PLoS ONE ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. e87024 ◽  
Author(s):  
Jing Yuan ◽  
David Ka Wai Yeung ◽  
Greta S. P. Mok ◽  
Kunwar S. Bhatia ◽  
Yi-Xiang J. Wang ◽  
...  
Keyword(s):  
Pilot Study ◽  
Nasopharyngeal Carcinoma ◽  
Head And Neck ◽  
Diffusion Weighted Imaging ◽  
Diffusion Weighted ◽  
Gaussian Analysis ◽  
Non Gaussian

Prospects for ACT: Simulations, power spectrum, and non-Gaussian analysis

New Astronomy ◽  
2005 ◽  
Vol 10 (6) ◽  
pp. 491-515 ◽  
Author(s):  
Kevin M. Huffenberger ◽  
Uroš Seljak
Keyword(s):  
Power Spectrum ◽  
Gaussian Analysis ◽  
Non Gaussian

scholarly journals Explicit forms of Wick tensor powers in general white noise spaces

2002 ◽  
Vol 31 (7) ◽  
pp. 413-420 ◽  
Author(s):  
ZhiyuanHuang Huang ◽  
Xiaoshan Hu ◽  
Xiangjun Wang
Keyword(s):  
White Noise ◽  
Random Variable ◽  
Nuclear Space ◽  
Infinitely Divisible ◽  
Gaussian Analysis ◽  
Tensor Powers

This paper is devoted to construction and investigation of explicit forms of Wick tensor powers in general white noise spaces. We give an extension of some objects and structure of Gaussian analysis to the case of more general white noise measures onE*(the dual of a nuclear spaceE), such that the random variable〈ω,ξ〉is infinitely divisible distributed for anyξ∈Eandω∈E*.

Gaussian analysis of Raman spectroscopy of acetic acid reveals a significant amount of monomers that effectively cooperate with hydrogen bonded linear chains

2014 ◽  
Vol 16 (41) ◽  
pp. 22458-22461 ◽  
Author(s):  
Jianping Wu
Keyword(s):  
Raman Spectroscopy ◽  
Hydrogen Bonding ◽  
Acetic Acid ◽  
Polymer Chains ◽  
Linear Chains ◽  
Gaussian Analysis ◽  
Hydrogen Bonded

Gaussian analysis of Raman spectroscopy reveals three hydrogen bonding structures in the liquid acetic acid (AA): linear chains, cyclic dimers and dissociated monomers that effectively cooperate with hydrogen bonded stacks of linear AA or polymer chains.

scholarly journals The semantic Stroop effect: An ex-Gaussian analysis

2016 ◽  
Vol 23 (5) ◽  
pp. 1576-1581 ◽  
Author(s):  
Darcy White ◽  
Evan F. Risko ◽  
Derek Besner
Keyword(s):  
Stroop Effect ◽  
Gaussian Analysis

Identification of Non-Gaussian Stochastic System

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
Sung-man Park ◽  
O-shin Kwon ◽  
Jin-sung Kim ◽  
Jong-bok Lee ◽  
Hoon Heo
Keyword(s):  
Random Process ◽  
Random Noise ◽  
Stochastic Simulations ◽  
Kolmogorov Equation ◽  
Gaussian Random Process ◽  
System Parameters ◽  
Analysis Technique ◽  
Gaussian Analysis ◽  
Gaussian System ◽  
Non Gaussian

This paper proposes a method to identify non-Gaussian random noise in an unknown system through the use of a modified system identification (ID) technique in the stochastic domain, which is based on a recently developed Gaussian system ID. The non-Gaussian random process is approximated via an equivalent Gaussian approach. A modified Fokker–Planck–Kolmogorov equation based on a non-Gaussian analysis technique is adopted to utilize an effective Gaussian random process that represents an implied non-Gaussian random process. When a system under non-Gaussian random noise reveals stationary moment output, the system parameters can be extracted via symbolic computation. Monte Carlo stochastic simulations are conducted to reveal some approximate results, which are close to the actual values of the system parameters.

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