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线性代数网课代修|最小二乘法代写least squares method辅导|ECON301

线性代数网课代修|最小二乘法代写least squares method辅导|ECON301

如果你也在 怎样代写线性代数Linear Algebra这个学科遇到相关的难题,请随时右上角联系我们的24/7代写客服。线性代数Linear Algebra是数学的一个分支,涉及到矢量空间和线性映射。它包括对线、面和子空间的研究,也涉及所有向量空间的一般属性。

线性代数Linear Algebra也被用于大多数科学和工程engineering领域,因为它可以对许多自然现象进行建模Mathematical model,并对这些模型进行高效计算。对于不能用线性代数建模的非线性系统Nonlinear system,它经常被用来处理一阶first-order approximations近似。

linearalgebra.me 为您的留学生涯保驾护航 在线性代数linear algebra作业代写方面已经树立了自己的口碑, 保证靠谱, 高质且原创的线性代数linear algebra代写服务。我们的专家在线性代数linear algebra代写方面经验极为丰富,各种线性代数linear algebra相关的作业也就用不着 说。

我们提供的线性代数linear algebra及其相关学科的代写,服务范围广, 其中包括但不限于:

  • 数值分析
  • 高等线性代数
  • 矩阵论
  • 优化理论
  • 线性规划
  • 逼近论
线性代数网课代修|最小二乘法代写least squares method辅导|ECON301

线性代数作业代写linear algebra代考|Statistical Learning

The term statistical learning is used to cover a broad class of methods and problems that have become feasible as the power of the computer has grown. An in-depth survey of this field is covered in a fairly recent book by Hastie, Tibshirani and Friedman entitled The Elements of Statistical Learning: Data Mining, Inference and Prediction [HA01]. Their book covers both supervised and unsupervised learning. The goal of supervised learning is to predict an output variable as a function of a number of input variables (or as they are sometimes called: indicators or predictors). In unsupervised learning there is no particular output variable and one is interested in finding associations and patterns among the variables. The cornerstone of statistical learning is to learn from the data. The analyst has access to data and his or her goal is to make sense out of the available information.

Supervised learning problems can be subdivided into regression and classification problems. The goal in regression problems is to develop quantitative predictions for the dependent variable. The goal in classification problems is to develop methods for predicting to which class a particular data point belongs. An example of a regression problem is the development of a model for predicting the unemployment rate as a function of economic indictors. An example of a classification problem is the development of a model for predicting whether or not a particular email message is a spam message or a real message. In this book, although classification problems are discussed (see Sections $2.8$ and 7.8), the emphasis is on regression problems.

线性代数作业代写linear algebra代考|THE METHOD OF LEAST SQUARES

The first published treatment of the method of least squares was included in an appendix to Adrien Marie Legendre’s book Nouvelles methods pour la determination des orbites des cometes. The 9 page appendix was entitled Sur la methode des moindres quarres. The book and appendix was published in 1805 and included only 80 pages but gained a 55 page supplement in 1806 and a second 80 page supplement in 1820 [ST86]. It has been said that the method of least squares was to statistics what calculus had been to mathematics. The method became a standard tool in astronomy and geodesy throughout Europe within a decade of its publication. The method was also the cause of a dispute between two giants of the scientific world of the $19^{\text {th }}$ century: Legendre and Gauss. Gauss in 1809 in his famous Theoria Motus claimed that he had been using the method since 1795. That book was first translated into English in 1857 under the authority of the United States Navy by the Nautical Almanac and Smithsonian Institute [GA57]. Another interesting aspect of the method is that it was rediscovered in a slightly different form by Sir Francis Galton. In 1885 Galton introduced the concept of regression in his work on heredity. But as Stigler says: “Is there more than one way a sum of squared deviations can be made small?” Even though the method of least squares was discovered about 200 years ago, it is still “the most widely used nontrivial technique of modern statistics” [ST86].

The least squares method is discussed in many books but the treatment is usually limited to linear least squares problems. In particular, the emphasis is often on fitting straight lines or polynomials to data. The multiple linear regression problem (described below) is also discussed extensively (e.g., [FR92, WA93]). Treatment of the general nonlinear least squares problem is included in a much smaller number of books. One of the earliest books on this subject was written by W. E. Deming and published in the pre-computer era in 1943 [DE43]. An early paper by R. Moore and R. Zeigler discussing one of the first general purpose computer programs for solving nonlinear least squares problems was published in 1960 [MO60]. The program described in the paper was developed at the Los Alamos Laboratories in New Mexico. Since then general least squares has been covered in varying degrees and with varying emphases by a number of authors (e.g., DR66, WO67, BA74, GA94, VE02).

线性代数网课代修|最小二乘法代写least squares method辅导|ECON301

线性代数作业代写linear algebra代考|Statistical Learning

统计学习一词用于涵盖随着计算机功能的增长而变得可行的广泛类型的方法和问题。Hastie、Tibshirani 和 Friedman 最近出版的一本名为《统计学习的要素:数据挖掘、推理和预测》[HA01] 的书对此领域进行了深入调查。他们的书涵盖了有监督和无监督学习。监督学习的目标是将输出变量预测为多个输入变量(或有时称为:指标或预测变量)的函数。在无监督学习中,没有特定的输出变量,人们对寻找变量之间的关联和模式感兴趣。统计学习的基石是从数据中学习。

监督学习问题可以细分为回归和分类问题。回归问题的目标是对因变量进行定量预测。分类问题的目标是开发预测特定数据点属于哪个类别的方法。回归问题的一个例子是开发一个模型来预测失业率作为经济指标的函数。分类问题的一个示例是开发用于预测特定电子邮件消息是垃圾邮件消息还是真实消息的模型。在本书中,虽然讨论了分类问题(参见章节2.8和 7.8),重点是回归问题。

线性代数作业代写linear algebra代考|THE METHOD OF LEAST SQUARES

第一次发表的最小二乘法处理方法包含在 Adrien Marie Legendre 的书 Nouvelles methods pour la determine des orbites des cometes 的附录中。9 页的附录题为 Sur la methode des moindres quarres。这本书和附录于 1805 年出版,仅包含 80 页,但在 1806 年获得了 55 页的增刊,并在 1820 年获得了第二次 80 页的增刊 [ST86]。有人说最小二乘法之于统计就像微积分之于数学。该方法在发布后的十年内成为整个欧洲天文学和大地测量学的标准工具。该方法也引起了科学界两大巨头之间的争执。19th 世纪:勒让德和高斯。Gauss 于 1809 年在其著名的 Theoria Motus 中声称他自 1795 年以来一直在使用该方法。该书于 1857 年在美国海军的授权下由航海年历和史密森学会 [GA57] 首次翻译成英文。该方法的另一个有趣方面是弗朗西斯·高尔顿爵士以稍微不同的形式重新发现了它。1885 年,高尔顿在他关于遗传的著作中引入了回归的概念。但正如斯蒂格勒所说:“是否有不止一种方法可以使偏差平方和变小?” 尽管最小二乘法是在大约 200 年前发现的,但它仍然是“现代统计学中使用最广泛的非平凡技术”[ST86]。

许多书籍都讨论了最小二乘法,但处理通常仅限于线性最小二乘问题。特别是,重点往往是对数据拟合直线或多项式。多元线性回归问题(如下所述)也被广泛讨论(例如,[FR92,WA93])。一般非线性最小二乘问题的处理包含在数量少得多的书籍中。关于这一主题的最早书籍之一是 WE Deming 撰写的,并于 1943 年在计算机出现之前的时代出版 [DE43]。R. Moore 和 R. Zeigler 于 1960 年发表了一篇早期论文,讨论了用于解决非线性最小二乘问题的第一个通用计算机程序之一 [MO60]。论文中描述的程序是在新墨西哥州的洛斯阿拉莫斯实验室开发的。

线性代数作业代写linear algebra代考| Non–singular matrices

线性代数作业代写 请认准UprivateTA™. UprivateTA™为您的留学生涯保驾护航。

抽象代数Galois理论代写

偏微分方程代写成功案例

代数数论代考

计量经济学代写

计量经济学是利用统计方法检验经济学理论的一种方法,它既不属于统计的范畴也不属于经济的范畴更像是一种经验科学。大家有专业的问题可以在my-assignmentexpert™ 这里答疑,多读一读,相关的基础性的东西,做一些统计和经济的基础知识的积累对于学习计量经济学这一门课程都是有很大帮助的。

统计作业代写

集合论数理逻辑代写案例

凸优化代写

统计exam代考

线性代数到底应该怎么学?

线代是一门逻辑性非常强的数学,非常注重对概念的深入理解,QS排名前200的大学普遍线性代数考试的题目80%以上都是证明题形式。而且初学的时候大家会觉得线代概念很乱很杂且环环相扣,学的时候经常要翻前面的东西。

在这种情况下,如何学好线性代数?如何保证线性代数能获得高分呢?

如何理清楚线代的概念,总结并且理解各个概念和定理之间的层次关系和逻辑关系是最关键的。具体实行方法和其他科目大同小异,书+记笔记+刷题,但这三个怎么用,在UrivatetaTA了解到的情况来说,我觉得大部分人对总结理解是不准确的,以下将说明我认为效率最高的的总结方法。

1.1 mark on book

【重点的误解】划重点不是书上粗体,更不是每个定义,线代概念这么多,很多朋友强迫症似的把每个定义整整齐齐用荧光笔标出来,然后整本书都是重点,那期末怎么复习呀。我认为需要标出的重点为

A. 不懂,或是生涩,或是不熟悉的部分。这点很重要,有的定义浅显,但证明方法很奇怪。我会将晦涩的定义,证明方法标出。在看书时,所有例题将答案遮住,自己做,卡住了就说明不熟悉这个例题的方法,也标出。

B. 老师课上总结或强调的部分。这个没啥好讲的,跟着老师走就对了

C. 你自己做题过程中,发现模糊的知识点

1.2 take note

记笔记千万不是抄书!!!我看到很多课友都是,抄老师的PPT,或者把书上的东西搬到笔记本上。有人可能觉得抄容易记起来,但数学不是背书嗷,抄一遍浪费时间且无用。我用我笔记的一小部分来说明怎么做笔记。

1.3 understand the relation between definitions

比如特征值,特征向量,不变子空间,Jordan blocks, Jordan stadard form的一堆定义和推论,看起来很难记,但搞懂他们之间的关系就很简单了

美本或者加拿大本科,如果需要期末考试之前突击线性代数,怎样可以效率最大化?

如果您是美本或者加拿大本科的学生,那么您的教材有很大概率是Sehldon Axler的linear algebra done right这本书,这本书通俗易懂的同时做到了只有300页的厚度,以几何的观点介绍了线性代数的所有基本且重要的内容.

从目录来看,这本书从linear vector space的定义讲起,引入线性代数这一主题,第二章开始将讨论范围限制在有限维的线性空间,这样做的好处是规避Zorn lemma的使用,在处理无穷维线性空间的过程中,取基不可避免的需要用到zorn lemma,第二章主要讲了independent set和basis的概念,同时引入了维数

前两章的内容可以看做是线性代数的启蒙阶段,理解了这两章就知道了线性代数研究的对象基本上是怎么回事,虽然还没有学任何non-trivial的内容,此时最重要的当然是linear vector space和independent set, basis, dimension的概念

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