Description: copua是金融数学计算中的一类新模型。本代码提供了最常用的copula模型，如clayton等中的参数估计等内容-copua financial mathematical calculation of a new type of model. This code provides the most commonly used model of Copulas, such as Clayton of parameter estimation etc. Platform: |
Size: 8192 |
Author:王璐 |
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Description: 使用高斯模型期望值最大化演算法,做圖形分割
Gaumix_EM: EM Algorithm Applicated to Parameter Estimation for Gaussian Mixture
-Gaussian model using expectation maximization algorithm, to do graphics segmentation Gaumix_EM: EM Algorithm Applicated to Parameter Estimation for Gaussian Mixture Platform: |
Size: 1024 |
Author:李致賢 |
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Description: 高斯混合模型参数估计,EM算法,sunMOG.m为函数,testMOG4.m为测试程序-Gaussian mixture model parameter estimation, EM algorithm, sunMOG.m for the function, testMOG4.m for the test procedure Platform: |
Size: 1024 |
Author:junsun |
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Description: 这个matlab程序用于实现广义高斯分布的参数估计，非常有用-Matlab program for the realization of the generalized Gaussian distribution of parameter estimation, a very useful Platform: |
Size: 7168 |
Author:吴 |
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Description: 自己编写的matlab程序，用于广义高斯分布的形状参数估计。-Matlab have written procedures for the generalized Gaussian distribution shape parameter estimation. Platform: |
Size: 132096 |
Author:lpmsu |
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Description: 提出了一种基于函数联接的感知器神经网络的纹理分类方法.它采用高斯2马尔柯夫随机场模型(GM RF)对纹理进行描述,模型参数即为纹理特征,参数估计采用最小平方误差方法获得.将估计参数作为表达纹理的特征向量,用感知器网络对特征进行分类,并且采用函数联接的方式解决线性不可分问题.对纹理图象进行的实验表明,采用这种方法能够提高学习速度,简化计算过程,并取得较好的纹理分类效果.
-Based on the function connected perceptron neural network texture classification method. It uses 2 Gaussian Markov Random Field Model (GM RF) to describe the texture, the model parameters is the texture feature, parameter estimation using least squares error obtained. the estimated parameters as the expression of texture feature vector, using the characteristics of sensor networks for classification, and the use of function to resolve connection problems can not be separated from linear. of texture images of the experiments show that this approach can enhance the learning speed, to simplify the calculation process and obtain a better effect of texture classification. Platform: |
Size: 285696 |
Author:singro jiang |
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Description: EM算法（英文）A Gentle Tutorial of the EM Algorithm
and its Application to Parameter
Estimation for Gaussian Mixture and
Hidden Markov Models-A Gentle Tutorial of the EM Algorithm
and its Application to Parameter
Estimation for Gaussian Mixture and
Hidden Markov Models Platform: |
Size: 99328 |
Author:雷雷 |
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Description: EM算法简明教程 用于高斯分布隐马尔可夫模型的参数估计-Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models Platform: |
Size: 99328 |
Author:hou |
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Description: 在实时平台上,高斯混合模型(GMM)具有计算有效性和易于实现的优点。最大似然规则中,模型参数不
断更新,但由于爬山特征,任意的原始模型参数估计通常将导致局部最优 遗传算法(GA)适于求解复杂组合优化问
题及非线性函数优化。提出了基于说话人识别的可以解决GMM局部最优问题的GMM/GA新算法,实验结果表明,
提出的GMM/GA新算法比纯粹的GMM算法能获得更优的效果。
- In real-time platform, the Gaussian mixture model (GMM) with the calculation of the effectiveness and easy to realize benefits. Maximum likelihood rule, the model parameters are not
Broken updates, but due to climbing features, any of the original model parameter estimation will usually result in local optimum genetic algorithm (GA) is suitable for solving complex combinatorial optimization question
Title and non-linear function optimization. Proposed speaker recognition based on GMM can solve the problem of local optimal GMM/GA new algorithm, experimental results show that the
Proposed GMM/GA new algorithm than purely GMM algorithm can get better results. Platform: |
Size: 4448256 |
Author:于高 |
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Description: 电子书
Two features of "Processing Random Data" differentiate it from other similar books: the focus on computing the reproducibility error for statistical measurements, and its comprehensive coverage of Maximum Likelihood parameter estimation techniques. The book is useful for dealing with situations where there is a model relating to the input and output of a process, but with a random component, which could be noise in the system or the process itself could be random, like turbulence. Parameter estimation techniques are shown for many different types of statistical models, including joint Gaussian. The Cramer-Rao bounds are described as useful estimates of reproducibility errors. Finally, using an example with a random sampling of turbulent flows that can occur when using laser anemometry, the book also explains the use of conditional probabilities.-Processing random data
Two features of "Processing Random Data" differentiate it from other similar books: the focus on computing the reproducibility error for statistical measurements, and its comprehensive coverage of Maximum Likelihood parameter estimation techniques. The book is useful for dealing with situations where there is a model relating to the input and output of a process, but with a random component, which could be noise in the system or the process itself could be random, like turbulence. Parameter estimation techniques are shown for many different types of statistical models, including joint Gaussian. The Cramer-Rao bounds are described as useful estimates of reproducibility errors. Finally, using an example with a random sampling of turbulent flows that can occur when using laser anemometry, the book also explains the use of conditional probabilities. Platform: |
Size: 4691968 |
Author:november |
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Description: This package provides a Maximum Entropy Modeling toolkit written in C++ with Python binding. It includes:
Conditional Maximum Entropy Model
L-BFGS Parameter Estimation
GIS Parameter Estimation
Gaussian Prior Smoothing
C++ API
Python Extension module
Document and Tutorial -)-This package provides a Maximum Entropy Modeling toolkit written in C++ with Python binding. It includes:
Conditional Maximum Entropy Model
L-BFGS Parameter Estimation
GIS Parameter Estimation
Gaussian Prior Smoothing
C++ API
Python Extension module
Document and Tutorial -) Platform: |
Size: 764928 |
Author:shabo |
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Description: 产生符合高斯分布N( u, a*a)的随机数，然后用参数估计法估计相应的 和 产生尺度参数为 的指数分布，并且估计参数 的取值和根据我介绍的方法产生GGD分布的随机数，形状参数为c =1.0-That meet the Gaussian distribution N ( u, a* a) of the random number, then the corresponding parameter estimation method to estimate and generated for the scale parameter of exponential distribution, and the values of estimated parameters, and according to my introduced GGD distribution method produces a random number, the shape parameter c = 1.0 Platform: |
Size: 2048 |
Author:五也 |
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Description: 对含噪声的图像进行识别和估计，通过小波分解，在频域中完成对图像的识别，判断是椒盐噪声还是高斯噪声，并对参数的值进行估计。-Identify and estimate noisy images, through the wavelet decomposition in the frequency domain to complete the image recognition to determine the salt and pepper noise or Gaussian noise, and estimate the value of the parameter. Platform: |
Size: 287744 |
Author:peterlox |
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Description: 本次实验主要是对一个正弦信号加入高斯白噪声，然后通过傅里叶变换对正弦信号进行谱估计。最后要用matlab进行仿真，得到正弦函数的时域和频域波形，关键找出信噪比和正弦信号频谱的均方误差之间的关系。-The experiment is a sinusoidal signal which is to white Gaussian noise, then by Fourier transform of the sinusoidal signal spectrum estimation. Finally, using the matlab simulation, sine function in time domain and frequency domain waveforms. Platform: |
Size: 39936 |
Author:zjc |
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Description: EM算法用于混合高斯模型的参数估计，并附有一个例子进行说明，程序解释-EM algorithm is used for of the gaussian mixture model parameter estimation, and with an example specification, process explanation Platform: |
Size: 1024 |
Author:唐苦 |
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Description: A Gentle Tutorial of the EM Algorithm
and its Application to Parameter
Estimation for Gaussian Mixture and
Hidden Markov Models Platform: |
Size: 88064 |
Author:asif |
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Description: 本书较详细地论述了信号的线性检测、假设检验理论、已知信号的检测，随机信号的检测、色高斯噪声中信号的检测、序列检测、恒虚警处理、非参量检测以及在线性处理时信号参量的估计和信号参量估计的一般理论等。-This book discusses in detail the signal linear detection, hypothesis testing theory, known signal detection, random signal detection, color gaussian noise of signal detection, sequence detection, constant false alarm processing, non parameter detection and the linear processing signal parameter estimation and signal parameter estimation of the general theory, etc. Platform: |
Size: 4673536 |
Author:张小豆 |
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Search - gaussian parameter estimation