贝叶

基本解释 (translations)

Bayeux

词组短语

pattra leaves

与贝叶相关的网络例句 [注：此内容来源于网络，仅供参考]

It not only analyses the metaphor mechanisms of AIS, but also describe the framework of immune algorithm and common immune algorithm. On the other hand the paper introduces the topological structure of Bayes network, assumption of the independence condition, the basic Bayes formula.

对人工免疫系统，不仅分析了它的仿生机理，还给出了免疫算法的基本架构，和常用的基于群体的免疫算法；对贝叶斯网络，给出了基本的贝叶斯公式，分析了贝叶斯网络的拓扑结构，和条件独立性假设。

One of the classification models that based on statistic theory is Bayesian network classifier.

目前已知的分类算法中一种重要的基于统计方法的模型是贝叶斯分类模型，在贝叶斯分类模型中实用性最高和应用最广泛的是朴素贝叶斯分类器。

On the basis of analyzing the classification principle of Bayesian classification model and a variant of Bayes theorem, a new classification model based on Bayes theorem, DLBAN (double-level Bayesian network augmented naive Bayes), which adds the dependence among attributes by selecting the key attributes, is proposed.

通过分析贝叶斯分类模型的分类原则以及贝叶斯定理的变异形式，提出了一种基于贝叶斯定理的新的分类模型DLBAN(double-level Bayesian network augmented naive Bayes)。该模型通过选择关键属性建立属性之间的依赖关系。

Firstly , using the conditional independence test built the Bayesian network structure , and desensitizes remote sensing data of study area . Secondly , applying Bayesian network theorem as classification principle classifies every single pixel to a pixel of maximum probability sort .

采用贝叶斯网络分类对具有典型干旱特征的库车县土壤盐渍化情况进行监测，首先应用条件独立性测试原理建立贝叶斯网络结构，把研究区遥感数据进行离散化，然后应用贝叶斯定理作为分类原则，将每个像元分为像元最大概率的类别。

The program design with the language of MATLAB on Bayes decision procedure is introduced in the paper.

利用MATLAB语言，可以完成贝叶斯决策的程序设计。贝叶斯决策有严谨的思路，是决策者经常使用的一种决策方法。贝叶斯决策在情况不明的状态下，能够做出得到最佳效果的决策

In Section 3,we give the simple narration aboutthe Bayesian analysis and Bayesian inference of the general linear model,and consider two specifica-tion of the general linear model.Based on these we give the point estimation,region estimation(includ-ing the high posterior density estimation)to the parameters and the predictive analysis of the generallinear model and so on.And in the final of this chapter we introduce some relative references about theBayesian analysis of Linear Model.

第三节就布罗米林对一般线性模型所作的贝叶斯分析与推断给出简单阐述，考虑了一般线性模型的两种设定，基于此给出一般线性模型参数的点估计、区域估计以及预测等贝叶斯分析和推断，并且介绍有关线性模型的贝叶斯分析的主要参考文献。

In this paper I introduce basic theory of Bayes' method and business risk. I give out the thought of essential solving steps, then combine with an instance, as a spinning mill which would produce two different manufactures, flower woolen cloth and gabardine. I adopt Bayes' expectation of loss method to analysis the two manufactures producing, then made a decision, figure out expectation value of the two schemes. Then select a plan which get best profit or least loss. I compute other indexes, for example, probabilities under different stations, tenderness analysis, risk degree of different plans, then compare those indexes, we make a decision.

在文中详细的介绍了贝叶斯方法的基础理论和企业风险的有关理论，给出了贝叶斯估计方法的基本解题思路和步骤，再结合具体实例，对某纺织厂公司生产两种产品，花呢和华达呢具体生产的决策问题采用贝叶斯期望损益分析法，计算出两种方案的期望值，选取收益最大或损失最小的决策方案为最优决策方案，在不同的自然状态下，再计算其他的指标，例如敏感度分析，风险度。

By the minimum risk Bayes decision theory, this paper develops a new way of image segmentation: establish the mathematics model of image segmentation; estimate the probability density of grey scales and figure out its math-expectation and square difference that accord with normal distribution and the loss function; and judge the every pixel dot in the image according to the minimum risk Bayes decision theory and determine whether it is of target or non-target images, thereby realizing the extraction of the target image.

依据最小风险贝叶斯决策理论，提出了一种基于最小风险贝叶斯决策的图像分割方法。首先建立图像分割的最小风险贝叶斯决策模型，对灰度级类条件概率密度估计出其符合正态分布的数学期望和方差以及损失函数，再依据最小风险贝叶斯决策理论对图像中的每一像素点进行目标图像和非目标图像的类别判断，从而实现目标图像的提取。

Further more, based on the analysis of Bayesian AR model and MA model , we analyzed the Bayesian ARMA model and its mathematical structure. Mainly analyzed the Bayesian ARMA(1,1) model, and constructed the model condition likelihood function and the parameters posterior distribution.

同时，以时间序列AR模型及MA模型的贝叶斯分析为基础进行了时间序列ARMA模型的贝叶斯分析，从分析ARMA模型的数学结构开始，重点进行了ARMA(1，1)模型的贝叶斯分析；构建了模型的条件似然函数和参数的先验分布，推导其参数的条件后验密度和边缘后验密度；并借用一组SAS软件模拟的ARMA(1，1)序列，通过WinBUGS进行仿真分析。

In Recent years, with the successful progresses in Bayesian statistics, especially in Bayesian computation, the Bayesian "machine", Bayesian analysis together with Bayesian computation, are unarguably the most powerful mechanism ever created for processing data and knowledge.

近几年来，随着贝叶斯统计学，尤其贝叶斯统计计算方法中一批重要方法的发展，&贝叶斯机&——贝叶斯分析加贝叶斯统计计算方法——无可争辩是最强大的处理数据和知识的机制。

与贝叶相关的网络解释 [注：此内容来源于网络，仅供参考]

bayes decision function：贝叶斯判定函数

batch processing 成批处理 | bayes decision function 贝叶斯判定函数 | bayes formula 贝叶斯公式

bayes decision function：贝叶斯决策函数

贝叶斯估计|Bayes estimate | 贝叶斯决策函数|Bayes decision function | 贝叶斯区间估计|Bayes interval estimate

bayes formula：贝叶斯公式

bayes decision function 贝叶斯判定函数 | bayes formula 贝叶斯公式 | bayes postulate 贝叶斯公设

bayes postulate：贝叶斯公设

bayes formula 贝叶斯公式 | bayes postulate 贝叶斯公设 | bayes solution 贝叶斯解

Bayes：贝叶斯

贝叶斯(Bayes)统计分析要使用贝页斯统计分析,必须要初始化贝页斯数据库. 根据具体需求收集适合系统的垃圾邮件和正常邮件词汇集,通过系统的" 贝叶斯(Bayes)数据库初始化" 功能进行贝页斯数据初始化

Bayes rule：贝叶斯规则

Bayes risk 贝叶斯风险 | Bayes rule 贝叶斯规则 | Bayes test 贝叶斯检验

Bayes law：贝叶斯定律

Bayes decision rule 贝叶斯决策规则,贝叶斯判决规则 | Bayes law 贝叶斯定律 | Bayes risk 贝叶斯风险

Bayes decision procedure：贝叶斯决策过程

Bayes classifier 贝叶斯分类器 | Bayes decision procedure 贝叶斯决策过程 | Bayes decision rule 贝叶斯决策规则

Bayesian robustness：贝叶斯稳健性

贝叶斯分类器:Bayesian classifiers | 朴素贝叶斯网络:Naive Bayesian Network | 贝叶斯稳健性:Bayesian robustness

Bayesian Statistical Model：贝叶斯统计模型

贝叶斯分类法:Bayesian classification | 贝叶斯统计模型:Bayesian Statistical Model | 贝叶斯优化算法:Bayesian Optimization Algorithm