1) conditional independence
条件随机独立性
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) conditional independence
条件独立性
1.
By using CI (conditional independence) tests, it can be pruned a fully connected potential graph to a best PG, which is expected to approximate the undirected version of the underlying directed graph.
阐述了贝叶斯网络结构学习的内容与方法 ,提出一种基于条件独立性 (CI)测试的启发式算法。
2.
It shows that the distribution determined by the Bayesian network maximises entropy given the causal and probability distribution of a Bayesian network under the conditional independence.
用信息熵的观点 ,如果将Bayesian网看作Agent的背景知识 ,采用与Bayesian网对应的概率分布作为信念函数的Agent的分布是最合理的 ,说明了与最大熵相对应的概率分布正好是在条件独立性假设下由Bayesian网确定的特征概率分布 。
5) Independence of random incident
随机事件的独立性
6) stochastically independent event
随机独立事件
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