random multiplicative noiseWe study the long time behaviour of a nonlinear oscillator subject to a random multiplicative noise with a spectral density (or power-spectrum) that decays as a power law at high freq
Random noise vs. White/Gaussian noiserand() is a MATLAB random number generator. It generates random variables that follow a uniform probability distribution. randn() generates random numbers that follow a Gaussian distribution.
如果假设data y 有noise(Gaussian with std \sigma),只需把上式所有 K(X,X) 换成K(X,X) + \sigma^2I_n 即可。 KL expansion (choosing basis) GP的sampling空间是linear space,那么就可以有basis functions的概念。如果选定一组(deterministic)basis function,randomness就可以从function转移到coefficients上。
网络高斯随机噪声;高斯随机杂讯 网络释义
turbulence as well as errors caused by imperfect optics and transmission. The resulting image degradations are frequently modelled as a mixture of additive white Gaussian noise, mainly responsible for dark and shot currents, and impulsive noise generating pixels with random channel intensity values3,9...
This paper describes the recognition method of random mass patterns corrupted by additive Gaussian noise. In our previous papers this method was applied to the recognition of abnormal shadows in chest roentgenograms and found to work effectively. In this paper we theoretically study the method from...
2022 TGRS Seismic Random Noise Attenuation Based on Non-IID Pixel-Wise Gaussian Noise Modeling. VI-non-IID can explicitly predict both the latent clean data and the pixelwise noise level map ($\bf{\sigma}$) - mengchuangji/VI-Non-IID
ut are εt are mutually independent and individually iid random standard Gaussian noise series. The linear state-space coefficient matrices are: A=[β0α1]. B=[σ0]. The observation equation is nonlinear. Because εt is a standard Gaussian random variable, the conditional distribution of yt ...
Seismic random noise attenuation based on non-IID pixel-wise Gaussian noise modeling Schematic diagram of the VI-non-IID framework VI-non-IID framework The input and output of VI-non-IID. Note: The predicted noise level map is very useful for analyzing the characteristics of the field seismic...
Initialize length scales of the kernel function at 10 and signal and noise standard deviations at the standard deviation of the response. Get sigma0 = std(ytrain); sigmaF0 = sigma0; d = size(Xtrain,2); sigmaM0 = 10*ones(d,1); Fit the GPR model using the initial kernel parameter...