在研究方法上,相对传统的向量自回归模型(Vector Autoregression Model,VAR)而言,加入贝叶斯先验信息(Bayesian Prior)所构建的贝叶斯向量自回归模型(BVAR)能较好地克服过度拟合的问题,取得较好的预测效果。
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吉伯斯分布作为一种引入图像空间信息的先验模型已广泛运用于贝叶斯图像分割中。
Gibbs distribution is a popular prior model widely used in Bayesian segmentation due to its excellent property describing the spatial information of image.
贝叶斯方式是依据新的信息从先验概率得到后验概率的一种方式。
Bayesian is one kind of method of posteriori probability obtained from priori probability according to new information.
为了从单次性的动态测试中获取最多的信息，引入了贝叶斯方法使实验者根据测试结果提供的新的信息修改其先验置信度。
For. obtaining the most information, we introduce bayesian method in order to modify their apriori creditability by experimenters by means of new information provided by measurement results.
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