网络释义 1. 高斯分布公式 formula ... Fresnel reflection formula 【光】菲涅耳反射公式Gaussian distribution formula高斯分布公式... dict.yqie.com|基于6个网页
complex Gaussian distribution 复高斯分布 Gaussian distribution curve 高斯粒度分布曲线 Gaussian distribution formula 高斯分布公式 Gaussian shaped concentration distribution 高斯型浓度分布 Gaussian curve 高斯曲线 相似单词 gaussian adj.(德国数学家)高斯的,高斯发现的,高斯提出的 Gaussian 高斯型,高斯型曲线...
换句话说,f(x) 服从一个多维正太分布,f(x*)服从一个多维正太分布,(f(x)’,f(x*)’)’仍然服从多维正太分布,然后呢,多维正太分布有一个非常好的性质,就是给定其中一部分,另一部分的条件分布还是一个正太分布(可以看这里Multivariate normal distribution)。
From the above formula for normal distribution, it can be inferred that about 68% of all values lie within one standard deviation from the mean; 95.4% of all values lie within two standard deviations from the mean and 99.7% of all values lie within three standard deviations from the mean....
Normal distribution ）. 再说Process,这个词语在数学上多指随机过程（Stochastic process ）.简单来讲，...
this distribution consists of zeros of certain Al-Salam–Chihara polynomials. To find them we refer to and expose known result concerning addition of q-exponential function. This leads to generalization of a well-known formula [Formula: see text], where H k (x) denotes kth Hermite polynomial....
It will be useful to summarize the relevantGaussianformulae. 有必要概括地讲一下有关的高斯公式. 辞典例句 Thegaussiandistribution is the familiar bell - shaped curve. 高斯分布是常见的钟罩形曲线. 辞典例句 Copy now the mask layer, applying to it Filter - Blur -GaussianBlur. ...
The normal or Gaussian distribution is often denoted byN(μ,σ2)N(μ,σ2). When a random variableXXis distributed normally with meanμμand varianceσ2σ2, we writeX∼N(μ,σ2)X∼N(μ,σ2). The formula for the distribution is ...
#Non-normal distributions: their prevention and cure#The Gaussian formula, for the mathematically minded#The Central Limit Theorem#More on histograms and probability distribution functions (pdfs)Statistics and Experimental Design for Psychologists:A model comparison approachRory Allen...
Now, if we have bivariate form of \(X = [x_1\ x_2]\), and also assume \(x_1\) and \(x_2\) are statistically independent, then we can get the joint distribution: \[ \begin{align*}\notag \mathrm{P}(x_1,x_2) &= \mathrm{P}(x_1)\mathrm{P}(x_2) \\ ...