Bayesian probability framework is a theory to transfer a priori probability to posterior probability. This thesis provides a formal Bayesian framework for image classification problems, which maps the low-level image features to the intrinsic high-level semantics.
贝叶斯概率框架是一种将先验概率转化为后验概率的理论框架,通过形式化的图像分类概率框架可以将低级图像特征映射到已有的高层语义。
参考来源 - 基于内容图像检索中图像语义分类技术研究·2,447,543篇论文数据，部分数据来源于NoteExpress
以上来源于: WordNet
In "A Plan for Spam" (see Resources later in this article), Graham suggested building Bayesian probability models of spam and non-spam words.
在“A Plan for Spam”（请参阅本文后面的 参考资料）中，Graham 提议建立垃圾邮件和非垃圾邮件单词的贝叶斯概率模型。
Bayesian network as, a very useful tool in data mining, can provide qualitative and quantitative relationship between attributes and probability inference.
贝叶斯网络是数据采掘的一个非常有效的工具，它能够定性和定量地分析属性之间的依赖关系，进行概率推理。
Based on the Bayesian theory, a probability method was proposed to estimate the parameters of the Poisson curve.
基于贝叶斯理论，提出采用概率的方法来估计泊松曲线的参数。
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