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.
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.
文中采用统计学理论，利用贝叶斯概率公式计算视频语义出现的概率，选取概率最大的类别标注未标记的样本。
In the paper, we use the statistical theory to calculate the probability of video semantics by Bayesian formula, choose the semantic of maximal probability to label the unlabeled samples.
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