Search - gaussian fitting algorithm

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[matlab] Matlab-Fitting-Functions DL : 0: 附件中的m代码包括了一系列的拟合函数，这些函数通常的输入是概率分布的样本。还有最大似然估计仿真器、最小平方仿真器、混合高斯分布估计的EM算法、-M in the annex to the code, including a series of fitting function, which function normally is the probability distribution of input samples. There are maximum likelihood estimation simulator, emulator least squares, mixed-Gaussian distribution is estimated that the EM algorithm,
Date : 2025-12-27 Size : 67kb User : liyuedsg
[matlab] FittFunc DL : 0: 多种概率分布的拟合函数集合本算法包括最大似然估计，最小二乘估计，基于EM算法的多种混合高斯分布估计，EM算法测试实例，绘制每种分布的plot函数。非常有参考价值！-Fitting a wide range of probability distribution functions including the collection of this algorithm maximum likelihood estimation, least squares estimation, based on the EM algorithm estimate a mixture Gaussian distribution, EM algorithm for the test examples, each mapping the distribution of the plot function. Have reference value is!
Date : 2025-12-27 Size : 21kb User : lcp
[matlab] Point_Curvature DL : 0: 估计一个离散点云中某个点的高斯曲率和离散曲率，算法是拟合一个二次曲面-Estimate a discrete point cloud to a point in the discrete Gaussian curvature and the curvature, the algorithm is a quadratic surface fitting
Date : 2025-12-27 Size : 1kb User : yanmohan
[matlab] code DL : 0: 对于单输入单输出的系统（Single input single output，SISO）常采用最小二乘方法辨识系统的参数。最小二乘参数估计是一个经典的方法，概念简明，适应范围广，来源于数理统计的回归分析，它能提供一个在最小方差意义上与实验数据最好拟合的模型，在一些情况下，可得到与极大似然法一样好的统计效果，并能很方便地与其它辨识算法建立关系。在一定条件下，最小二乘法参数估计法有最佳的统计特性，即一致的、无偏的和有效的结果。本代码主要关于使用递推最小二乘辨识方法与增广最小二乘辨识方法辨识模型参数，采用高斯噪声作为系统的噪声。(For Single input Single output (SISO), the least squares method is used to identify the parameters of the system. Least squares parameter estimation is a classic method, concept is concise, wide adaptation, derived from the regression analysis of mathematical statistics, it can provide a minimum variance sense the best fitting model with the experimental data, in some cases, the statistics can be obtained with the maximum likelihood method is as good effect, and can easily establish relations with other identification algorithm. Under certain conditions, the least square parameter estimation method has the best statistical properties, namely consistent, unbiased and effective results. This code mainly USES the method of recursive least squares identification method and the augmented least squares identification method to identify the model parameters, using gaussian noise as the noise of the system.)
Date : 2025-12-27 Size : 1kb User : M.R.J.