inverse gaussian距母函数 Inverse Gaussian分布,也称为逆高斯分布,是一种连续概率分布。它的概率密度函数(pdf)为: f(x) =√(π/ 2) * e^(-(x^2) / 2) * x 其中,x≥0。 逆高斯分布的累积分布函数(cdf)可以通过数值积分求解,但通常使用数值方法进行计算。 逆高斯分布的距离母函数(distance generating ...
A first-order autoregressive process with one-dimensional inverse Gaussian marginals is introduced. The innovation distributions are obtained in certain special cases. The unknown parameters are estimated using different methods and these estimators are shown to be consistent and asymptotically normal. ...
InverseGaussianDistribution[μ,λ,θ] 表示一个参数为μ、λ和θ的广义逆高斯分布. 更多信息 背景 范例 打开所有单元 基本范例(6) 概率密度函数: In[3]:= Out[3]= In[2]:= Out[2]= 一个逆高斯分布的累积分布函数: In[1]:= Out[1]=
2.6 The normal inverse gaussian process Barndorff and Nielsen (1998) proposed the normal inverse Gaussian (NIG) distribution as a possible model for the stock price. This process may also be represented as a time-changed Brownian motion, where the time change T(t) is the first passage time ...
网络逆高斯 网络释义 1. 逆高斯 ...进的Erlang2-Gamma分布(CNBD);用逆高斯(Inverse Gaussian)分布来描述购买时间间隔[19];或者用对数正态分布[20]来描 … lunwen.1kejian.com|基于4个网页 例句 释义: 全部,逆高斯
1. PDF generalized inverse Gaussian distribution (GIG) 是一个三参数的连续型概率分布: f(x)=(a/b)p/22Kp(ab−−√)xp−1e−(ax+b/x)/2,x>0 Kp(⋅):表示二阶(second kind)的修正的贝塞尔函数(modified Bessel functions),p表示索引,其两个参数a,b≥0 ...
Anatomy of the generalized inverse Gaussian-poisson distribution with special applications to bibliometric studies The vast number of observed bibliometric and scientometric datasets display a definite downward deviation from a straight line in the upper tail, when plot... HS Sichel - 《Information ...
Inverse-Gaussian apodized fiber Bragg gratings (IGAFBGs) are numerically studied using the transfer matrix method and fabricated by the commonly used phase-mask scanning technique in a single-step scanning process. The IGAFBG can serve as a dual-wavelength passband filter, whose wavelength spacing ...
clear clc % Gaussian-Jordan method to calculate inverse matrix n = 3; % dimension a = [2 2 3 % your matrix 1 -1 0 -1 2 1]; if det(a)==0 disp('singular matrix!') end aextend = zeros(n,2*n); for i = 1:n for j = 1:n aextend(i,j) = ...
In this article, we present a new class of models, which is generated from symmetrical distributions in mathbbR{mathbb{R}} and generalize the well known inverse Gaussian distribution. Specifically, the density, distribution function, properties, transformations and moments of this new model are ...