CPD:条件概率分布
条件概率分布(Conditional Probability Distribution,常缩写为CPD)是指某一随机变量在给定其他变量取值情况下的概率分布。为便于书写与使用,该术语在跨学科领域(如统计学、机器学习和人工智能)中广泛采用其英文缩写CPD。其核心思想在于描述变量间的条件依赖关系,是概率图模型和贝叶斯推断中的基础概念,对于建模复杂系统中的不确定性具有重要作用。
Conditional Probability Distribution具体释义
Conditional Probability Distribution的英文发音
例句
- At last the learning method for conditional probability distribution is investigated.
- 最后还提出了条件概率学习方法。
- The missing measurements are described by a binary switching sequence satisfying a conditional probability distribution.
- 系统的不完全量测用满足特定条件概率分布(CPD)的二值切换的随机序列来描述。
- This paper deals with the calculating method of the Pearson type ⅲ conditional probability distribution curve of streamflow.
- 本文讨论径流皮尔逊Ⅲ型条件概率分布(CPD)曲线的计算方法。
- Existence of B-valued random variable with regular conditional probability distribution
- B值随机变量正则条件概率分布(CPD)的存在性
- Bayesian network is a directed acyclic graph, it can use a conditional probability distribution directly express the dependencies between variables.
- 贝叶斯网络是一个有向无环图,能够通过一个条件概率分布(CPD)直观地表达各个变量之间的依赖关系。
本站英语缩略词为个人收集整理,可供非商业用途的复制、使用及分享,但严禁任何形式的采集或批量盗用
若CPD词条信息存在错误、不当之处或涉及侵权,请及时联系我们处理:675289112@qq.com。