posterior probability

Research on Image Matching Similarity Criterion Based on Maximum Posterior Probability

基于最大后验概率的图像匹配相似性指标研究

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This dissertation uses Semi-naive Bayesian model as the basic model and the Dirichlet distributive features of parameters to estimate the expected value of the posterior probability parameter.

以半朴素贝叶斯模型为基础贝叶斯模型，利用参数的Dirichlet分布特性，选取后验期望估计参数值。

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Based on which, Posterior Probability measure is developed.

据此, 建立了最大后验概率指标.

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The fault diagnosis arithmetic based on Bayes test method and the significance-test of time statistics of the transition was put forward. Under the guidance of modular fault diagnosis idea, stratify and locate the fault source combined with prior probability and posterior probability of the system.

提出了基于Bayes试验方法的故障诊断算法，对变迁的时间统计量进行显著性检验，结合系统的先验和后验概率，在模块化故障诊断思想的指导下，分层定位至故障源。

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In Bayes Statistical Inference, the Posterior Probability about Experiments Make Up Hemigroup

在Bayes统计推断中后验概率关于试验构成半群

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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。

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At first, the population sample data are split into subpopulation data sets over the whole test duration by using the posterior belonging probability of each observation to each subpopulation.

利用对每个子总体的每次观察的后验概率将整个检验期间的总体样本数据分成子总体数据集。

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In the process, we calculate the posterior probability of semantics by unlabeled samples information.

在计算的过程中, 使用了未标记样本的信息计算语义出现的后验概率.

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Mandarin Digit String Recognition Based on Segment Model Using Posterior Probability Decoding

基于后验概率解码段模型的汉语语音数字串识别

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In the first, calculate the posterior probability value and make hard decision.

首先，计算接收序列的后验概率值并作硬判决处理。

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It proves that the posterior probability about experiments makes up hemigroup then proves that the posterior probability of the order experiments and the accumulation experiments are the same.

证明了后验概率关于试验构成半群，从而序贯试验与累积试验具有相同的后验概率。

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Considering K Nearest Neighbor ( KNN) method has high accuracy but poor efficiency, this paper proposes a text categorization method based on the guidance of posterior probability named B-KNN.

针对K最近邻（KNN）方法分类准确率高但分类效率较低的特点，提出基于后验概率制导的贝叶斯K最近邻（B-KNN）方法。

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Bayse classify is a statistical method based on Bayes theorem, which uses prior probability and sample information to calculate the posterior probability.

贝叶斯分类是一种基于贝叶斯定理的统计学分类方法，它是结合先验信息与样本信息计算出后验概率。

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Another benefit of Posterior Probability measure is the low computational complexity and obvious global maximum.

这种指标的另一突出优点是计算复杂度很低,容易得到全局最优解.

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