...conditional independence, Markov property, density factorisation, copula, pair-copula construction, regu[gap=210]关键词：图形化模型，有向无环图，有条件的独立性，马氏性，密度因子分解，动词，对系词建设，定期藤，似然推断。
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Conditional Independence Assumption 条件独立性假设
conditional independence assumptions 条件独立性假设
conditional independence ci 条件独立性
conditional independence hypothesis 条件独立性假设
conditional independence ci test 条件独立性测试
hypothesis of conditional independence 条件独立性假设
Model-Powered Conditional Independence Test 模型驱动下的条件性独立性测试
Naive Bayes algorithm is an effective simple classification algorithm. Since its conditional independence assumption is not always true in real life,its classification performance is affected to some extent.
朴素贝叶斯算法是一种简单而高效的分类算法,但其条件独立性假设并不符合客观实际,这在某种程度上影响了它的分类性能。
参考来源 - 基于Rough Set的加权朴素贝叶斯分类算法 in C·2,447,543篇论文数据，部分数据来源于NoteExpress
以上来源于: WordNet
The identifiability for causal effects under a type of assumptions based on conditional independence in a causal model is treated by equation method.
运用方程组求解的方法来解决一类因果效应可识别的充要条件的问题。
Absrtact: Naive Bayesian classifier is a simple and effective classifier, but its conditional independence assumption makes it unable to express the dependence among features.
摘要：朴素贝叶斯分类器是一种简单而高效的分类器，但它的条件独立性假设使其无法表示属性问的依赖关系。
If the NB conditional independence assumption actually holds, a Naive Bayes classifier will converge quicker than discriminative models like logistic regression, so you need less training data.
倘若条件独立性假设确实满足，朴素贝叶斯分类器将会比判别模型，譬如逻辑回归收敛得更快，因此你只需要更少的训练数据。
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