1) independence
[英][,ɪndɪ'pendəns] [美]['ɪndɪ'pɛndəns]
随机独立
1.
Conditional regressive independence and conditional independence;
条件回归独立性与条件随机独立性
2) Random Independence
随机独立性
1.
The relation between regressive independence and random independence is discussed,and several necessary and sufficient conditions are presented.
本文讨论了回归独立性与随机独立性之间的关系，得到了两者等价的几个充分必要条件。
3) Independent random vectors
独立随机元
4) independent random variables
独立随机变量
1.
On the Basis of a these results, the Egorov s results for independent random variables are generalized to the case of negatively associated random variables.
给出了具有不同分布的NA随机变量列满足的若干强大数律;作为应用,不仅将独立随机变量的一类强极限定理完整的推广到NA随机变量情形,而且关于NA随机变量的一些已有结果可以作为推论得出。
2.
According to the Wittman strong law of large numbers of independent random variables,the Wittman strong law of large numbers of PA random variables sequences is extanded so that some deductions are obtained in this paper.
文章根据独立随机变量序列的Wittmann型强大数律,推广到PA序列的Wittmann型强大数律,并且由此得到一些相关的推论。
3.
In this paper,we consider asymptotic structure for the product of partial sums of independent random variables.
假设X1,X2,…,Xn,…为二阶矩存在的非负独立随机变量列,证明收敛性nk=1!μSkk"#1γk$%1&Tn→d e&2N成立,其中N是标准正态随机变量,Sk=ki=1(Xi,μk=E(Sk),σk=Var(Sk),γk=σk/μk,且Tn=nk=1(k/σk。
5) Independent Random Variable
独立随机变量
1.
Central Limit Theorems of Independent Random Variables;
独立随机变量的中心极限定理
2.
Formula of density function of sum of independent random variable of uniform distribution;
服从均匀分布的多个独立随机变量和的密度函数公式
3.
Let {Xn,n≥1} be independent random variables in a real separable Banach space,and the Chung-Teicher type conditions for the SLLN under the assumptions that the weak laws of large numbers hold were doscissed,which is b-1n∑nk=1(Xk-EXkI(‖Xk‖≤bk))p0 holds if and only if b-1n∑nk=1(Xk-EXkI(‖Xk‖≤bk))a.
设{Xn,n≥1}是实可分Banach空间独立随机变量,讨论了在弱大数律的假设下使得Chung-Teicher型强大数律也成立,即bn-1∑nk=1(Xk-EXkI(‖Xk‖≤bk))p0当且仅当bn-1∑nk=1(Xk-EXkI(‖Xk‖≤bk))a。
6) independent random process
独立随机过程
1.
A necessary and sufficient condition for an identifying independent random process is that the finitedimensional probability density function of the process can be expressed with a product of some onedimensional functions.
识别独立随机过程的一个充要条件是独立随机过程的有限维概率密度函数,可表示为若干个一维函数的乘积,这些一维函数只与相对应的边缘概率密度函数相差一个常数,且其中有关的边界常数均与自变量和参数无关。
补充资料：独立增量随机过程
独立增量随机过程
tochastic process with independent increments
独立增里随机过程「劝刘巨浦c拌.义冠弓初山侧吻创如t加盆，曰n臼lts;cjl抖浦.咸nP0uecc c Ite3洲cltMuM.uP-“P啊eHll，刚』 一种随机过程(s勿比邵石cp~)X(t)，对任意自然数”和所有实数O蕊:，<口，簇:2<吞2簇…蕊，。<口。，增量X(乃;)一X(‘J)，…，X(刀。)一X(，。)是相互独立随机变量，独立增量随机过程称为齐次的(holll。罗11印us)，如果X(:+h)一X(。)，0(戊，o
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条