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density function相关的网络例句

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与 density function 相关的网络例句 [注:此内容来源于网络,仅供参考]

By the geometric probability model,the intuitionistic method is provided for the marginal density function and conditional probability density function .

利用几何概型得出均匀分布的边缘密度函数和条件概率密度函数的直观求法。

Normal distribution in statistics is based on the probability density function in the form of representation, and set up a continuous random variable X, the probability density function f has the form of Figure 1, where a, b are constants and were known as the average value and standard deviation,π, e, respectively pi and natural constants, claimed that X subject to parameters a, b of the normal distribution.

正态分布在统计学中是以概率密度函数的形式表述的,设连续型随机变量X的概率密度函数f有图1中的形式,其中a、b为常数,分别被称为平均值和标准差,π、e 分别为圆周率和自然常数,则称X服从参数为a,b的正态分布。

The current sensitivity probability density function of energetic materials was deduced based on Frank-Kamenetskii parameter and resistance supposing as stochastic variable of normal distribution, by the application of the probability density formula of function in two random variables.

将Frank-Kamenetskii参数和电阻假设为正态分布的随机变量,应用二维随机变量函数的概率密度公式,推导出了含能材料电流点火感度的概率密度函数。

According to this joint probability density function, the probability density function of the ambiguity residual vector is obtained.

根据这个联合概率密度函数得到了模糊度残差向量的概率密度。

In Chapter 5,Use the logarithmic derivative formula of Г function, analysis the characters of X2distributed density function in detail, and the effects of change of the parameter on density curve.

利用Γ函数对数微商公式,较详细地分析了X~2分布密度函数的性质,研究参数n对密度函数曲线的影响。

Supposeis two-dimension random variable, is unity density function and marginal density function leave each other, then sufficient and necessary condition for independence of and is: for all real values ,there are .

Theorem 3.3 设是二维连续型随机变量,分别是联合密度函数与边缘密度函数,则相互独立的充要条件是:对任意的实数,都有。

The variation of density with depth is expressed as a function of n order polynomial function to deduce general gravity anomaly formula for 2 D density variable body having arbitrary and regular shapes.

本文把密度随深度的变化表示成任意次多项式函数,推导出了计算任意和规则形状二度变密度体重力异常的通用解析公式。

The noise frequency modulation signal was particularly analyzed. The Fokker-Planck equation of noise frequency modulation was presented and the Motion-Group Fourier Transform was used by converting the partial differential equation into the variable coefficient homogenous linear differential equations. Then the solutions were given by using the peano-baker series. So the probability density function of noise frequency modulation in the filter was given. On the basis of the probability density function the stochastic resonance of noise frequency modulation signal is analyzed.

首先建立了噪声调频干扰信号通过脉冲压缩雷达中频滤波器后所满足的福克尔-普朗克方程,然后利用群移傅立叶变换(Motion-Group Fourier Transform,MGFT)将此偏微分方程化成了变系数齐次线性微分方程组,并利用Peano-Baker级数法给出了该方程组的解,最后得到了噪声调频干扰信号通过脉冲压缩雷达中频滤波器后的概率密度函数,在此基础上研究了噪声调频干扰信号造成的随机共振现象。

I have calculated the species diversity for 3 layers (i.e. tree layer, shrub layer and herb layer) by means of various biodiversity index formulas and analyzed the relative species abundance using 9 models of the probability density distribution functions, such as, 3 Distribution (or Beta Distribution, Weibull Distribution, Lognormal Distribution, Poisson Distribution, Binomial Distribution, Negative Binomial Distribution ,Geometric Distribution, etc.. chi-square analyses were conducted on species distribution by using the chi-square test formulated by Pearson to test which distribution function is better, the result of chi square test made it possible to reject the other 8 distribution functions, theβdistribution function performs better than other probability density functions, it has a very close approximation, which can be used for the description of relative abundances of species in forest communities in this data set.

在相对多度研究上选用了九种概率分布模型,这九种概率密度分布函数依次为:贝塔分布、Weibull分布、对数正态分布、泊松分布、二项分布、负二项分布、几何分布、对数分布和奈曼A型分布,并进行了严格的卡方检验,结果表明:其它八种分布均被遭到拒绝,只有贝塔分布获得了通过,且拟合的结果非常理想。

In the method, the probability density function of a stochastic process is described from the spectral density function of the stochastic process.

根据S。 O。 Rice对随机过程峰的一维概率分布的研究方法,扩展到对随机过程峰的联合概率密度的分析。

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