Bayesian inference

基本解释

The most common statistical approach is called bayesian inference and is explained in detail in another IBM developerWorks article (see Resources).
最常见的统计方式称为贝叶斯推理，更详细的内容，可以参阅 IBM developerWorks 的另一篇文章（参见参考资料）。
Bayesian inference has also gained prominence in e-mail spam-fighting, and in non-spam-related areas such as customer affinity engines (or, commercial recommendation engines).
贝叶斯推理在对付电子邮件中的垃圾信息以及其他与垃圾信息不相关的领域（比如客户亲和力引擎，或商业推荐引擎）均有突出的表现。
The algorithm handles uncertain information with Bayesian inference, giving a quantitative evaluation of the security state of a system and eliminating false alarms effectively.
算法用贝耶斯推理处理不确定信息，量化地评估系统安全状态，并且有效地消除误报。

贝叶斯推理
This paper proposes a new image segmentation algorithm based on watershed transformation combined color information and uses Bayesian inference on this watershed image.
提出了一个结合彩色信息的分水线图像分割方法,在LUV颜色空间的分水线图像上,使用贝叶斯推理的图像分割方法。
贝叶斯推断
A Bayesian inference is based on posterior densities of the parameters, namely for the j-th parameterθ_j the conditional density .
一个贝叶斯推断是基于后验密度函数的参数的,也就是说对第j个参数θ_j的条件密度为。
贝斯推理

贝叶斯推断
By the time-discretization approach, this paper derives a stochastic difference equation from the geometric mean-reversion process and then a nonlinear regression model is established. By this way, the paper obtains the distribution and estimation for each parameter by Bayesian inference.
针对连续几何均值回复模型,通过时间离散化导出对应的随机差分方程,从而得到一个非线性回归模型,进而利用贝叶斯推断得到各个参数的估计及后验分布。
bayes估计