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贝叶斯优化算法是一种新的演化算法，通过贝叶斯概率统计的知识来学习后代，可是使演化朝有利的方向前进，程序用C实现了贝叶斯优化算法。-Bayesian Optimization Algorithm is a new evolutionary algorithm, through Bayesian probability and statistics to learn the knowledge of future generations, but to enable the evolution to
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用于计算贝叶斯频谱概率，可用于实际工程！！!-used to calculate the Bayesian probability spectrum can be used for actual works! ! !
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The BNL toolbox is a set of Matlab functions for defining and estimating the
parameters of a Bayesian network for discrete variables in which the conditional
probability tables are specified by logistic regression models. Logistic regression can be
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在具有模式的完整统计知识条件下，按照贝叶斯决策理论进行设计的一种最优分类器。分类器是对每一个输入模式赋予一个类别名称的软件或硬件装置，而贝叶斯分类器是各种分类器中分类错误概率最小或者在预先给定代价的情况下平均风险最小的分类器。-In a model under the condition of complete statistical knowledge, in accordance with the Bayesian decision theory to design an optimal c
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基于贝叶斯理论的指纹识别算法及学习套件, 使用贝叶斯概率论实现对指纹识别,特征码提取,特征对数获取的功能-Based on Bayesian theory and learning algorithm for fingerprint identification kits, the use of Bayesian probability theory to achieve fingerprint, signature extraction, characteristics of the func
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贝叶斯网络概率中文分词算法，基于概率的分词算法-Bayesian network probability of Chinese word segmentation algorithm, based on the probability of word segmentation algorithm
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这系列课件系统地讲述了模式识别的基本理论和基本方法。内容涵盖了贝叶斯决策、概率密度函数的估计、线性判别函数、邻近法则、特征的选择和提取、非监督学习、神经网络、模糊模式识别等。-This series of courseware on a pattern recognition system to the basic theory and basic methods. Covers the Bayesian decision-making, the estimated probability de
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贝叶斯网络是一种概率网络，它是基于概率推理的图形化网络，而贝叶斯公式则是这个概率网络的基础。贝叶斯网络是基于概率推理的数学模型,所谓概率推理就是通过一些变量的信息来获取其他的概率信息的过程，基于概率推理的贝叶斯网络(Bayesian network)是为了解决不定性和不完整性问题而提出的，它对于解决复杂设备不确定性和关联性引起的故障有很的优势，在多个领域中获得广泛应用。本算法用于weka算法包的拓展。-Bayesian network is a probabilistic network, wh
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建立贝叶斯网络，草地湿的条件概率网络。计算边缘概率，联合分布。-The establishment of Bayesian networks, conditional probability network of wet grass. Computing marginal probabilities, the joint distribution.
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突变分为如下主要的几种：均值突变（最常见）、方差突变、线性回归突变（也称趋势突变）、概率突变、空间型突变、谱突变、模型参数突变，等等。贝叶斯突变检测属于概率突变检测方法，其特点是能给出突变点的概率分布图。-Mutations are divided into the following main categories: the mean mutation (the most common), variance mutation, linear regression mutation (also
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This the MRICP driver for player/stage. This Driver is used as a localizer and Occupancy Grid Map Builder using simple Bayesian Probability Update. It uses Iterative closest Point method to allign laser scans and estimate the change of pose-This is t
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BAYESIAN STATISTICS by Sarat C. Dass Department of Statistics & Probability Department of Computer Science & Engineering Michigan State University-BAYESIAN STATISTICS by Sarat C. Dass Department of Statistics & Probability Department of Com
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贝叶斯学习概率的算法设计Bayesian probability algorithm-Bayesian probability Bayesian probability algorithm algorithm
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用监督参数估计中的贝叶斯方法估计条件概率密度的参数u-With the supervision of the Bayesian estimation method to estimate the parameters of the conditional probability density of u
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实现贝叶斯最大后验概率计算，适合原理的研究，简单易懂。-To achieve the maximum a posteriori Bayesian probability, the principle of appropriate and easily understood.
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贝叶斯学习理论使用概率去表示所有形式的不确定性，学习和推理都通过概率规则来实现。-Bayesian learning theory using the probability that all forms of uncertainty, learning and reasoning by probability rules . Bayesian learning , the results of the probability distribution of random variables ,
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实现贝叶斯概率计算过程。假设进行n次试生产，计算最后得到后验概率，即可信度。样本数据集个数和观测值从文件中读入，计算过程封装为动态链接库，以.dll形式提供，
主程序提供界面输入先验分布值、边缘分布值，显示计算结果。
-Bayesian probability calculation process. Assuming that the n-th trial production, and calculate the final posterior probability that cr
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Builds BN from topological
structure information and probability model
and predict diagnose from evidences using
BN inference
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j简单的贝叶斯模型数据概率求解问题，有助于理解贝叶斯模型数据分析过程。-j simple Bayesian probability model data to solve problems, help to understand the Bayesian model data analysis process.
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LDA是一种文档主题生成模型，也称为一个三层贝叶斯概率模型，包含词、主题和文档三层结构。文档到主题服从Dirichlet分布，主题到词服从多项式分布。
LDA是一种非监督机器学习技术，可以用来识别大规模文档集（document collection）或语料库（corpus）中潜藏的主题信息。它采用了词袋（bag of words）的方法，这种方法将每一篇文档视为一个词频向量，从而将文本信息转化为了易于建模的数字信息。但是词袋方法没有考虑词与词之间的顺序，这简化了问题的复杂性，同时也为
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