## 机器学习代写|机器学习代写machine learning代考|COMP30027

2022年10月13日

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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础
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## 机器学习代写|机器学习代写machine learning代考|Genomic Breeding Values and Their Estimation

In plant and animal breeding, it is a common practice to rank and select individuals (plants or animals) based on their true breeding values (TBVs), also called additive genetic values. However, since we cannot see genes and breeding values, this task is not straightforward, and it is therefore estimated indirectly using observed phenotypes. The estimated values are called estimated breeding values (EBVs), which means that TBV is a latent variable that is only approximated using the observable variable (phenotype).

When the TBVs are used, the genetic change is expected to be larger than when the EBVs are used, but this difference is small when the EBVs are accurately estimated. EBVs reflect the true genetic potential or true genetic transmitting ability of individuals (plants or animals). Traditionally, they are estimated based on the performance records of their parents, sibs, progenies, and their own after correcting for various environmental factors such as management, season, age, etc. When parents are selected based on their breeding values with high reliability, a faster genetic progress is expected in the resulting population. For this reason, the process of estimating breeding values is of paramount importance in any breeding program.
There are several methods to estimate genomic estimated breeding values (GEBVs), but first we will describe the best linear unbiased predictor (BLUP) method. When using the BLUP method to estimate the GEBVs, we need to use the mixed model equations (2.2) described above to estimate BLUEs and BLUPs. Using this equation (2.2) but depending on the form taken by the matrices $\boldsymbol{Z}$ and $\boldsymbol{\Sigma}$, we can end up with the GBLUP method or the SNP-BLUP method to estimate the breeding values. First, we explain the GBLUP method, where we substitute $\boldsymbol{Z}$ and $\boldsymbol{\Sigma}$ matrices for the incidence matrix of genotypes and genomic relationship matrix (GRM) derived from allele frequencies calculated with one of the methods of VanRaden (2008) given in Sect. 2.4. Under this GBLUP method, the GEBV can be obtained as the solution $\hat{\boldsymbol{u}}$ of the mixed model equation:
$$\left(\begin{array}{c} \widehat{\boldsymbol{\beta}} \ \widehat{\boldsymbol{u}} \end{array}\right)=\left(\begin{array}{cc} \boldsymbol{X}^{\mathrm{T}} \boldsymbol{R}^{-1} \boldsymbol{X} & \boldsymbol{X}^{\mathrm{T}} \boldsymbol{R}^{-1} \mathbf{1} \ \mathbf{1}^{\mathrm{T}} \boldsymbol{R}^{-1} \boldsymbol{X} & \mathbf{1}^{\mathrm{T}} \boldsymbol{R}^{-1} \mathbf{1}+\boldsymbol{\sigma}_g^{-2} \boldsymbol{G}^{-1} \end{array}\right)^{-1}\left(\begin{array}{c} \boldsymbol{X}^{\mathrm{T}} \boldsymbol{R}^{-1} \boldsymbol{y} \ \mathbf{1}^{\mathrm{T}} \boldsymbol{R}^{-1} \boldsymbol{y} \end{array}\right),$$
where $Z$ was replaced by $Z=1$ and $\boldsymbol{\Sigma}$ by $\sigma_g^2 G$, the genomic relationship matrix that was calculated with some of the methods described in Sect.

## 机器学习代写|机器学习代写machine learning代考|Normalization Methods

This section describes four types of normalization variables (inputs and outputs). In this case, normalization refers to the process of adjusting the different inputs or outputs that were originally measured in different scales to the same scale. It is very important to carry out the normalization process before giving the inputs and outputs for most statistical machine learning algorithms because it helps improve the numerical stability in the estimation process of some algorithms; it is suggested mostly when the inputs or outputs are in different scales. However, it is important to point out that in some statistical machine learning software, the normalization process is done internally, in which case this process does not need to be carried out manually. The five normalization methods we describe next are centering, scaling, standardization, max normalization, and minimax normalization.

Centering This normalization consists of subtracting from each variable (input or output) its mean, $\mu$; this means that the centered values are calculated as
$$X_i^=X_i-\mu$$ Thẻ cênteréd variablè $X_i^$ has a meañ ô zeroo.
Scaling This normalization consists of dividing each variable (input or output) by its standard deviation, $\sigma$. The scaled values are calculated as
$$X_i^=\frac{X_i}{\sigma} .$$ The scaled variable $X_i^$ has unit variance.
Standardization This process of normalization consists of calculating its mean, $\mu$, and standard deviation, $\sigma$, for each input or output. The standardized values are then calculated as
$$X_i^*=\frac{X_i-\mu}{\sigma} .$$
This process is carried out for each input or output variable, and this needs to be done with care, since we need to use the corresponding mean and standard deviation of each variable. The output of the standardized score has a mean of zero and a variance of one, which means that most standardized values range between $-3.5$ and $3.5$.

# 机器学习代考

## 机器学习代写|机器学习代写machine learning代考|基因组育种值及其估计

.

$$\left(\begin{array}{c} \widehat{\boldsymbol{\beta}} \ \widehat{\boldsymbol{u}} \end{array}\right)=\left(\begin{array}{cc} \boldsymbol{X}^{\mathrm{T}} \boldsymbol{R}^{-1} \boldsymbol{X} & \boldsymbol{X}^{\mathrm{T}} \boldsymbol{R}^{-1} \mathbf{1} \ \mathbf{1}^{\mathrm{T}} \boldsymbol{R}^{-1} \boldsymbol{X} & \mathbf{1}^{\mathrm{T}} \boldsymbol{R}^{-1} \mathbf{1}+\boldsymbol{\sigma}_g^{-2} \boldsymbol{G}^{-1} \end{array}\right)^{-1}\left(\begin{array}{c} \boldsymbol{X}^{\mathrm{T}} \boldsymbol{R}^{-1} \boldsymbol{y} \ \mathbf{1}^{\mathrm{T}} \boldsymbol{R}^{-1} \boldsymbol{y} \end{array}\right),$$
，其中$Z$被$Z=1$取代，$\boldsymbol{\Sigma}$被$\sigma_g^2 G$取代，基因组关系矩阵是用节中描述的一些方法计算出来的

## 机器学习代写|机器学习代写machine learning代考|归一化方法

$$X_i^=X_i-\mu$$ Thẻ cênteréd variablè $X_i^$有一个meañ ô零。这种归一化包括将每个变量(输入或输出)除以其标准差$\sigma$。缩放值计算为
$$X_i^=\frac{X_i}{\sigma} .$$缩放变量$X_i^$具有单位方差。标准化的过程包括计算每个输入或输出的平均值$\mu$和标准差$\sigma$。然后计算标准化值为
$$X_i^*=\frac{X_i-\mu}{\sigma} .$$

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。