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先验分布

与 先验分布 相关的网络例句 [注:此内容来源于网络,仅供参考]

In the paper, the parameters of the prior data distribution are given by numerical calculation.

本文用数值计算的方法,得到了先验数据的分布参数。

It establishes the prior information, according to historical information or the design experiences and the posterior distribution of the parameters on the basis of sample data, by which we can do point estimation or interval estimation, predict the future trend.

首先,作为可靠性评估的基础性工作,本文提出了贝叶斯预测模型用于配网元件原始参数的评估,引入由历史资料或设计经验形成的先验信息,并结合样本数据形成被估参数的后验分布;在此基础上进行点估计或区间估计,就可以对未来的变化趋势做出预测。

The prior information of pixel intense distribution is introduced. Then simulated annealing algorithm is applied to choose the proper neighborhood structure, and the optimal estimate can be obtained.

引入像素强度的先验概率分布模型,运用模拟退火算法选择合适的邻域结构,获得强度的最优估计。

The prior probability can be estimated by using the equivalence of the Markov random fields and the Gibbs Distribution.

先验概率可以根据马尔可夫随机场和吉布斯分布的等效性,用GD的概率估计。

It has no strict precondition of normal distribution of the input data and can increase the classification accuracy efficiently though adjusting the prior probability density dynamically.

贝叶斯网络是一种新的数据表达和推理模型,对数据没有严格的正态分布前提要求,通过动态地调整先验概率密度,能有效提高分类精度。

Topics covered include subjective notion of probability, Bayes' Theorem, prior and posterior distributions, and data analysis techniques for statistical models.

包括:概率的主观定义,贝叶斯理论,先验和后验分布,统计模型的数据分析技术。

The modified D-S combines the advantage of the two arithmetics—G-S and D-S, it defines the function of belief renewably by using the information of the prophetic probability distributing to define the subject and the correlation degree's realization of the fuzzy aggregate.

修正的D-S方法则结合了前两类算法的优点,利用包含在先验概率分布中的信息,通过定义模糊集合的&包含度&和&相关度&实现,并将置信函数重新定义。

The output result is used as apriori information when the input is a full white image to calculate the distribution of spots and illumination ununiformity of which stabilization is assumed in an experiment.

利用系统输入全白图像的输出结果作为先验信息,在假定一次实验中污点和光源保持不变的基础上,确定其分布。

Because there are the characteristics of uncertainty and mixing meta-pixels in the remote-sensing images, the classical fuzzy c-means method has a low accuracy in the remote-sensing image clustering. This paper improves the fuzzy c-means method. And it adds a-priori information into the patterns to change the method as a semi-supervised clustering. In the clustering process, the unlabelled patterns compare similarities with the labeled patterns, and then the accuracy of the algorithm can be increased.(3)The paper proposes an interactive learning-based image mining in remote sensing.

由于遥感图像各类别在特征空间中散点图的分布的特点,本文对传统的FCM聚类算法进行改进,并且加入先验信息之后,将原来的非监督的聚类变成一种半监督的聚类方法,通过与已标签的样本进行相似性比较,能有效地提高聚类算法的准确度。

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然而,正如其名字所指出的那样,CD盘不能写,也不能用任何方式改变其内容。

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