7

黄金分割法的MatLab源码！！！黄金分割法的MatLab源码！！！(Golden section method of MatLab source code! ! ! Golden section method of MatLab source code! ! !)

Returns the Multilook Amplitude Ratio for correlated channels(Returns the Amplitude Ratio for Multilook correlated channels)

ultrasound video of a uterus

apply a lowpass filter to a watermarked audio signal to test the robustness input : vector representing the watermarked audio file output : vector representing the watermarked audio file corrupted by a lowpass filter

说明：  高斯过程(GP)模型中推理和预测的实现。它实现了在《Rasmussen & Williams:机器学习的高斯过程》(麻省理工学院出版社，2006)和《Nickisch & Rasmussen:二进制高斯过程分类的近似》(JMLR, 2008)中讨论的算法。该函数的优点在于灵活性、简单性和可扩展性。该函数具有一定的灵活性，首先通过定义均值函数和协方差函数来确定遗传算法的性质。其次，它允许指定不同的推理过程，如精确推理和期望传播(EP)。第三，它允许指定似然函数，如高斯函数或拉普拉斯函数(用于回归)和累积逻辑函数(用于分类)。简单性是通过一个简单的函数和紧凑的代码实现的。可扩展性是通过模块化设计来保证的，允许为已经相当广泛的推理方法、均值函数、协方差函数和似然函数库轻松添加扩展。(Gaussian Processes for Machine Learning , the MIT press, 2006 and Nickisch & Rasmussen: Approximations for Binary Gaussian Process Classification , JMLR, 2008. The strength of the function lies in its flexibility, simplicity and extensibility. The function is flexible as firstly it allows specification of the properties of the GP through definition of mean function and covariance functions. Secondly, it allows specification of different inference procedures, such as e.g. exact inference and Expectation Propagation (EP). Thirdly it allows specification of likelihood functions e.g. Gaussian or Laplace (for regression) and e.g. cumulative Logistic (for classification). Simplicity is achieved through a single function and compact code.)

说明：  matlab计算皮尔逊相关系数、斯皮尔曼相关系数、正态分布检验等。(The Pearson correlation coefficient, Spearman correlation coefficient and normal distribution test were calculated by MATLAB.)

2_d TMz wav propagation in free space

An effieient matlab program code for plotting the minimum vaiance froniers under different scenarios in the field of portfolio optimization.

介绍一个稳健性分析工具箱。主要做稳健性主成分、主成分回归、分类。(Our toolbox currently contains implementations of robust methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST-LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), principal component regression (RPCR), partial least squares (RSIMPLS) and classification (RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets.)

学习abaqus子程序umat的绝好例子，权威专家编写(Learning excellent examples of abaqus umat subroutine, leading experts to write)