### 统计代写|机器学习作业代写machine learning代考| Summary and Historical Remarks

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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

## 统计代写|机器学习作业代写machine learning代考|Summary and Historical Remarks

Bayesian classifiers calculate the product $P\left(\mathbf{x} \mid c_{i}\right) P\left(c_{i}\right)$ separately for each class, $c_{i}$, and then label $\mathbf{x}$ with the class where this product has the highest value.
The main problem is how to calculate the probability, $P\left(\mathbf{x} \mid c_{i}\right)$. Most of the time, the job is simplified by the assumption that the attributes are mutually independent, in which case $P\left(\mathbf{x} \mid c_{i}\right)=\prod_{j=1}^{n} P\left(x_{j} \mid c_{i}\right)$, where $n$ is the number of attributes.

The so-called $m$-estimate makes it possible to take advantage of a user’s prior idea about an event’s probability. This comes handy in domains with small training sets where relative frequency is unreliable.
In domains with continuous attributes, the role of discrete probability, $P\left(\mathbf{x} \mid c_{i}\right)$, is taken over by $p_{c_{i}}(\mathbf{x})$, the probability density function, $p d f$. Otherwise, the procedure is the same: the example is labeled with the class that maximizes the product, $p_{c_{i}}$ (x) $P\left(c_{i}\right)$.
The concrete shape of the $p d f$ is approximated by discretization, or by the use of standardized $p d f$ s, or by the sum of Gaussian functions.

## 统计代写|机器学习作业代写machine learning代考|Give It Some Thought

1. How would you employ $m$-estimate in a domain with three possible outcomes, $[A, B, C]$, each with the same prior probability estimate, $\pi_{A}=\pi_{B}=\pi_{C}=1 / 3 ?$ What if you trust your expectations of $A$ while not being so sure about $B$ and $C$ ? Is there a way to reflect this circumstance in the value of the parameter $m$ ?
2. Explain under which circumstances the accuracy of probability estimates benefits from the assumption that attributes are mutually independent. Explain the advantages and disadvantages.
3. How would you calculate the probabilities of the output classes in a domain where some attributes are Boolean, others discrete, and yet others continuous? Discuss the possibilities of combining different approaches.

## 统计代写|机器学习作业代写machine learning代考|Computer Assignments

Machine-learning researchers often test their algorithms on publicly available benchmark domains. A large repository of such domains can be found at the following address: www. ics.uci. edu/ mlearn/MLRepository. html. Take a look at these data and see how they differ in the numbers of attributes, types of attributes, sizes, and so on.
Write a computer program that uses the Bayes formula to calculate the class probabilities in a domain where all attributes are discrete. Apply this program to our “pies” domain.
For the case of continuous attributes, write a computer program that accepts the training examples in the form of a table such as the one from Exercise 3 above. Based on these, the program approximates the $p d f$ s and then uses them to determine the class labels of future examples.
Apply this program to a few benchmark domains from the UCI repository (choose from among those where all attributes are continuous) and observe that the program succeeds in some domains better than in others.

## 统计代写|机器学习作业代写machine learning代考|Give It Some Thought

1. 你会如何雇佣米- 在具有三种可能结果的域中进行估计，[一种,乙,C]，每个都有相同的先验概率估计，圆周率一种=圆周率乙=圆周率C=1/3?如果你相信你的期望一种虽然不太确定乙和C? 有没有办法在参数值中反映这种情况米 ?
2. 解释在哪些情况下概率估计的准确性受益于属性相互独立的假设。说明优点和缺点。
3. 在某些属性为布尔属性、其他属性为离散属性、其他属性为连续属性的域中，您将如何计算输出类的概率？讨论结合不同方法的可能性。

## 广义线性模型代考

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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。