在“APlanforSpam”（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。
In "A Plan for Spam" (see Resources later in this article), Graham suggested building Bayesian probability models of spam and non-spam words.
贝叶斯网络是在不确定性环境下有效的知识表示方式和概率推理模型，是一种流行的图形决策化分析工具。
Bayesian Networks is a model that efficiently represents knowledge and probabilistic inference and is a popular graphics decision-making analysis tool.
所提出计算模型为贝叶斯网的概率推理提供了一种新的局部计算方法。
The proposed computation models will supply new local computation methods for Bayesian network probabilistic inferences.
一个参数概率模型用于得到SBM谱密度，一个贝叶斯框架用于统计更新到本地记录。
A parametric probabilistic model is sought for the SBM spectral densities, and a Bayesian framework is used to statistically update it to local records.
在贝叶斯统计中计算一组竞争模型的后验概率及其相关贝叶斯因子一直是一个较难且有挑战性的课题。
Calculating posterior probabilities and related Bayes factors for a collection of competing models has been a difficult and challenging problem for Bayesian statisticians.
本文主要涉及的不确定推理模型包括主观贝叶斯的概率推理模型，可信度理论推理模型，证据理论及其改进推理模型以及神经网络推理模型。
In the paper, the models of uncertain reasoning are focused, such as the reasoning model of Bayes probability, Reliability theory, D-S evidence theory and Neural Network.
对这些查询建立贝叶斯网络模型，通过模型推导出各个查询在当前文档集合下的概率公式。
Then a Bayesian network model is built for all these queries, and the probability formula of each structured query given the document collection is inferred in the model.
依据这一模型，该方法使用贝叶斯理论和领域约束获得了区域和边界的最大后验概率估计。
The method is to derive the maximum a posteriori estimate of the regions and the boundaries by using Bayesian inference and neighborhood constraints based on Markov random fields(MRFs) models.
依据这一模型，该方法使用贝叶斯理论和领域约束获得了区域和边界的量大后验概率估计。
The method is to derive the maximum a posteriori estimate of the regions and the boundaries by using Bayesian inference and neighborhood constraints based on Markov random fields (MRFs) models.
其中在时间贝叶斯网络研究中，分别提出了适用于非循环时间贝叶斯网络的基于模型化简的概率更新算法和一般概率更新算法。
The probability updating algorithm for acyclic Temporal Bayesian network based on model simplification and general probability updating algorithm for Temporal Bayesian network are presented.
采用贝叶斯最大后验概率估计的方式，从统一背景模型中生成说话人模型。
We use Bayesian maximum a posteriori estimation training a speaker model from background model, to solve the problem of model miss matching in speaker verification system.
针对目标综合识别过程中的复杂性和不确定性特点，利用贝叶斯网络融合模型对融合识别过程进行概率建模及推理。
For dealing with the complexity and uncertainty in the target identification fusion, the fusion model of Bayesian Networks is used.
贝叶斯模型是一种计算模型，而不是描述模型，所以，也有人将其单独归为概率解释。
Bayesian model is a computational model, not a descriptive model. Hence, it's also returned to the probability interpretation.
贝叶斯模型是一种计算模型，而不是描述模型，所以，也有人将其单独归为概率解释。
Bayesian model is a computational model, not a descriptive model. Hence, it's also returned to the probability interpretation.
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