8 月 2022 - 统计代写答疑辅导

月度归档： 2022 年 8 月

金融代写|投资组合代写Investment Portfolio代考|FIN428

Posted on 2022年8月29日2022年8月29日 by statistics-lab

如果你也在怎样代写投资组合Investment Portfolio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。

投资组合是由投资人或金融机构所持有的股票、债券、金融衍生产品等组成的集合。目的是分散风险。投资组合可以看成几个层面上的组合。

statistics-lab™ 为您的留学生涯保驾护航在代写投资组合Investment Portfolio方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写投资组合Investment Portfolio方面经验极为丰富，各种代写投资组合Investment Portfolio相关的作业也就用不着说。

我们提供的投资组合Investment Portfolio及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等楖率论
Advanced Mathematical Statistics 高等数理统计学
(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

金融代写|投资组合代写Investment Portfolio代考|FIN428

金融代写|投资组合代写Investment Portfolio代考|CHALLENGES FOR INVESTMENT PROFESSIONALS

New trends are reshaping the investment industry and the expectations of investment professionals. Such change is not new; the contracts between professions and society are constantly redefined. The erosion of trust in financial markets and institutions is shown in the “2017 Edelman Trust Barometer” (Edelman Intelligence 2017), which found “the largestever drop in trust across the institutions of government, business, media and NGOs [nongovernmental organizations].” More specifically, the broad area of financial services ranked as the sector least trusted in both 2016 and 2017 when compared with the energy, consumer packaged goods, food and beverage, and technology sectors. The financial services industry is increasingly viewed as not managing conflicts of interest well.

Regulation has become increasingly commonplace across the world, in large part following the 2007-2008 global financial crisis, because of serious ethical breaches that occurred in financial markets and institutions. Some regulators now require investment advisers to belong to a professional body, adhere to an acceptable code of conduct, achieve a minimum level of relevant education, commit to continuing professional development, and comply with sanctions for any wrongdoing. Regulation has helped raise professional standards by making them a requirement for practice, although sometimes at the expense of the autonomy and flexibility required for professions to adapt and evolve.

Globalization has resulted in more common practice around the world and greater international harmonization of investment practices and regulation. This has, in turn, allowed global professional organizations, such as CFA Institute, to develop and expand their reach. Globalization may bring challenges, however, if large global investment firms seek to establish their own standards and practices that conflict with the codes of individual professional bodies.

Perhaps the greatest challenge for the investment profession comes from technology. Rapid advances in computing power, data storage, and internet connectivity threaten to alter the definition of professional expertise and how it is applied to serve investors. Already index replication, risk management, trade execution, asset allocation, algorithmic trading, and quantitative investment management are being automated, replacing or enhancing many of the functions of an investment professional. Developments in artificial intelligence are likely to accelerate this trend. The effects are many, from the need for evolved continuing education programs to new codes of practice and standards that recognize the growing intersection between human and artificial skill.

Making ethical decisions in an environment filled with challenges can be daunting. Practice with applying a framework for ethical decision-making can help prepare you for that.

金融代写|投资组合代写Investment Portfolio代考|BIG DATA

As noted, datasets are growing rapidly in terms of the size and diversity of data types that are available for analysis. The term Big Data has been in use since the late 1990 s and refers to the vast amount of data being generated by industry, governments, individuals, and electronic devices. Big Data includes data generated from traditional sources-such as stock exchanges, companies, and governments-as well as non-traditional data types, also known as alternative data, arising from the use of electronic devices, social media, sensor networks, and company exhaust (data generated in the normal course of doing business).

Traditional data sources include corporate data in the form of annual reports, regulatory filings, sales and earnings figures, and conference calls with analysts. Traditional data also include data that are generated in the financial markets, including trade prices and volumes. Because the world has become increasingly connected, we can now obtain data from a wide range of devices, including smart phones, cameras, microphones, radio-frequency identification (RFID) readers, wireless sensors, and satellites that are now in use all over the world. As the internet and the presence of such networked devices have grown, the use of nontraditional data sources, or alternative data sources-including social media (posts, tweets, and blogs), email and text communications, web traffic, online news sites, and other electronic information sources-has risen.
The term Big Data typically refers to datasets having the following characteristics:

Volume: The amount of data collected in files, records, and tables is very large, representing many millions, or even billions, of data points.
Velocity: The speed with which the data are communicated is extremely great. Real-time or near-real-time data have become the norm in many areas.
Variety: The data are collected from many different sources and in a variety of formats, including structured data (e.g., SQL tables or CSV files), semi-structured data (e.g., HTML code), and unstructured data (e.g., video messages).
Features relating to Big Data’s volume, velocity, and variety are shown in Exhibit 1 .
Exhibit 1 shows that data volumes are growing from megabytes (MB) and gigabytes (GB) to far larger sizes, such as terabytes (TB) and petabytes (PB), as more data are being generated, captured, and stored. At the same time, more data, traditional and non-traditional,are available on a real-time or near-real-time basis with far greater variety in data types than ever before.

Big Data may be structured, semi-structured, or unstructured data. Structured data items can be organized in tables and are commonly stored in a database where each field represents the same type of information. Unstructured data may be disparate, unorganized data that cannot be represented in tabular form. Unstructured data, such as those generated by social media, email, text messages, voice recordings, pictures, blogs, scanners, and sensors, often require different, specialized applications or custom programs before they can be useful to investment professionals. For example, in order to analyze data contained in emails or texts, specially developed or customized computer code may be required to first process these files. Semi-structured data may have attributes of both structured and unstructured data.

投资组合代考

金融代写|投资组合代写Investment Portfolio代考|CHALLENGES FOR INVESTMENT PROFESSIONALS

新趋势正在重塑投资行业和投资专业人士的期望。这种变化并不新鲜。职业与社会之间的契约不断被重新定义。“2017 年爱德曼信任晴雨表”（Edelman Intelligence 2017）显示了对金融市场和机构的信任度下降，该指标发现“政府、企业、媒体和非政府组织 [非政府组织] 机构的信任度下降幅度最大”。更具体地说，与能源、包装消费品、食品和饮料以及科技行业相比，金融服务这一广泛领域在 2016 年和 2017 年均被列为最不信任的行业。金融服务行业越来越被视为未能妥善管理利益冲突。

由于金融市场和机构发生严重的道德违规行为，监管在世界范围内变得越来越普遍，这在很大程度上是在 2007-2008 年全球金融危机之后。一些监管机构现在要求投资顾问属于专业机构，遵守可接受的行为准则，达到最低水平的相关教育，致力于持续的专业发展，并遵守对任何不当行为的制裁。监管通过使专业标准成为实践的要求，从而帮助提高了专业标准，尽管有时以牺牲专业适应和发展所需的自主性和灵活性为代价。

全球化导致全球范围内的实践更加普遍，投资实践和监管更加国际协调。这反过来又使全球专业组织（例如 CFA 协会）得以发展和扩大其影响范围。然而，如果大型全球投资公司寻求建立自己的标准和做法，与个别专业机构的准则相冲突，全球化可能会带来挑战。

也许投资行业最大的挑战来自技术。计算能力、数据存储和互联网连接的快速发展可能会改变专业知识的定义以及如何将其应用于为投资者服务。指数复制、风险管理、交易执行、资产分配、算法交易和量化投资管理已经实现自动化，取代或增强了投资专业人士的许多职能。人工智能的发展可能会加速这一趋势。影响是多方面的，从需要不断发展的继续教育计划到新的实践准则和标准，以认识到人类和人工技能之间日益增长的交叉点。

在充满挑战的环境中做出道德决策可能会令人生畏。练习应用道德决策框架可以帮助您为此做好准备。

金融代写|投资组合代写Investment Portfolio代考|BIG DATA

如前所述，就可用于分析的数据类型的大小和多样性而言，数据集正在迅速增长。大数据一词自 1990 年代后期以来一直在使用，指的是由行业、政府、个人和电子设备生成的大量数据。大数据包括从传统来源（如证券交易所、公司和政府）产生的数据，以及由使用电子设备、社交媒体、传感器网络和公司产生的非传统数据类型（也称为替代数据）尾气（正常经营过程中产生的数据）。

传统数据源包括年度报告、监管文件、销售和收益数据以及与分析师的电话会议形式的公司数据。传统数据还包括金融市场产生的数据，包括交易价格和交易量。由于世界变得越来越紧密，我们现在可以从广泛的设备获取数据，包括智能手机、相机、麦克风、射频识别 (RFID) 阅读器、无线传感器和卫星，这些设备现在遍布全球世界。随着互联网和此类联网设备的出现，非传统数据源或替代数据源的使用——包括社交媒体（帖子、推文和博客）、电子邮件和文本通信、网络流量、在线新闻网站和其他电子信息源——已经兴起。
术语大数据通常是指具有以下特征的数据集：

体积：文件、记录和表中收集的数据量非常大，代表了数百万甚至数十亿个数据点。
速度：数据传输的速度非常快。实时或近实时数据已成为许多领域的常态。
多样性：数据是从许多不同的来源以各种格式收集的，包括结构化数据（例如，SQL 表或 CSV 文件）、半结构化数据（例如，HTML 代码）和非结构化数据（例如，视频消息） .
与大数据的数量、速度和多样性相关的特征如图表 1 所示。
图表 1 显示，随着越来越多的数据被生成、捕获和存储，数据量正在从兆字节 (MB) 和千兆字节 (GB) 增长到更大的大小，例如太字节 (TB) 和 PB (PB)。与此同时，更多的传统和非传统数据可以实时或接近实时地获得，数据类型的多样性比以往任何时候都多。

大数据可以是结构化、半结构化或非结构化数据。结构化数据项可以组织在表格中，并且通常存储在数据库中，其中每个字段代表相同类型的信息。非结构化数据可能是无法以表格形式表示的不同的、无组织的数据。非结构化数据，例如由社交媒体、电子邮件、短信、录音、图片、博客、扫描仪和传感器生成的数据，通常需要不同的、专门的应用程序或自定义程序才能对投资专业人士有用。例如，为了分析电子邮件或文本中包含的数据，可能需要专门开发或定制的计算机代码来首先处理这些文件。半结构化数据可能同时具有结构化和非结构化数据的属性。

金融代写|投资组合代写Investment Portfolio代考请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考

随机过程代考

在概率论概念中，随机过程是随机变量的集合。若一随机系统的样本点是随机函数，则称此函数为样本函数，这一随机系统全部样本函数的集合是一个随机过程。实际应用中，样本函数的一般定义在时间域或者空间域。 随机过程的实例如股票和汇率的波动、语音信号、视频信号、体温的变化，随机运动如布朗运动、随机徘徊等等。

贝叶斯方法代考

贝叶斯统计概念及数据分析表示使用概率陈述回答有关未知参数的研究问题以及统计范式。后验分布包括关于参数的先验分布，和基于观测数据提供关于参数的信息似然模型。根据选择的先验分布和似然模型，后验分布可以解析或近似，例如，马尔科夫链蒙特卡罗 (MCMC) 方法之一。贝叶斯统计概念及数据分析使用后验分布来形成模型参数的各种摘要，包括点估计，如后验平均值、中位数、百分位数和称为可信区间的区间估计。此外，所有关于模型参数的统计检验都可以表示为基于估计后验分布的概率报表。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

statistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

机器学习代写

随着AI的大潮到来，Machine Learning逐渐成为一个新的学习热点。同时与传统CS相比，Machine Learning在其他领域也有着广泛的应用，因此这门学科成为不仅折磨CS专业同学的“小恶魔”，也是折磨生物、化学、统计等其他学科留学生的“大魔王”。学习Machine learning的一大绊脚石在于使用语言众多，跨学科范围广，所以学习起来尤其困难。但是不管你在学习Machine Learning时遇到任何难题，StudyGate专业导师团队都能为你轻松解决。

多元统计分析代考

基础数据: $N$ 个样本， $P$ 个变量数的单样本，组成的横列的数据表
变量定性: 分类和顺序；变量定量：数值
数学公式的角度分为: 因变量与自变量

时间序列分析代写

随机过程，是依赖于参数的一组随机变量的全体，参数通常是时间。随机变量是随机现象的数量表现，其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值（如1秒，5分钟，12小时，7天，1年），因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中，往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录，以得到其自身发展的规律。

回归分析代写

多元回归分析渐进（Multiple Regression Analysis Asymptotics）属于计量经济学领域，主要是一种数学上的统计分析方法，可以分析复杂情况下各影响因素的数学关系，在自然科学、社会和经济学等多个领域内应用广泛。

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，工具箱允许您学习和应用专业技术。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。

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

金融代写|投资组合代写Investment Portfolio代考|NBA5120

Posted on 2022年8月29日2022年8月29日 by statistics-lab

如果你也在怎样代写投资组合Investment Portfolio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。

投资组合是由投资人或金融机构所持有的股票、债券、金融衍生产品等组成的集合。目的是分散风险。投资组合可以看成几个层面上的组合。

我们提供的投资组合Investment Portfolio及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等楖率论
Advanced Mathematical Statistics 高等数理统计学
(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

金融代写|投资组合代写Investment Portfolio代考|NBA5120

金融代写|投资组合代写Investment Portfolio代考|EXPECTATIONS OF INVESTMENT PROFESSIONALS

Thus, the issue faced by CFA charterholders and other financial industry participants is not choosing between professional values and business values. Rather, it is balancing that ever-competing pair in a way that places the best interests of consumers and clients above our own corporate and personal interests.

All investment professionals …. would do well to … develop a keener awareness of the “big picture” of our financial system, a profound introspection into how we can make it better, a knowledge of the long history of finance, and a deep involvement in fostering in our profession the highest character it requires if we are to serve investors effectively, honestly, and prudently in the years ahead.
-John C. Bogle, “Balancing Professional Values and Business Values” (2017)
Characteristics and behavior expected of all professionals include honesty, integrity, altruism, continuous improvement, excellence, loyalty, and respect for colleagues, employers, and clients. Extremely high standards, but not perfection, are expected, including behavior in public. Professionals should, through their actions, uphold the reputation of their profession and be responsible, accountable, and reliable in their work. Professionals should reflect regularly about the cycle of self-improvement, starting with a self-assessment, identification of knowledge gaps, compiling a program of continuing professional development to fill those gaps, putting the new learning into practice, and then evaluating the results in order to inform the next cycle. Key duties of professionals are to provide independent advice, avoid or disclose conflicts of interest, and respect client information, objectivity, transparency, and confidentiality.

The behaviors expected will vary by profession, but some attributes are shared by most professions. These include the duty to be honest and open in dealings, which covers all aspects of professional practice, ranging from writing resumes, presenting advice, record keeping, and achieving-to the greatest extent possible-informed consent from clients. This means ensuring that clients understand the consequences of decisions, the range of outcomes, and risks. A client will not have the level of knowledge that the expert professional does, but the professional should not abuse this more specialized knowledge. There will at times be adverse outcomes for clients arising from errors in judgment and practice. Professionals should be prepared to acknowledge mistakes promptly, learn from them, and correct them, including making clients whole, where appropriate. Some professional codes state that professionals must disassociate themselves from any violation of laws or regulations. This means that individuals may need to act themselves in response to a concern even if they are unable to alter the conduct of others.

金融代写|投资组合代写Investment Portfolio代考|Description of the Framework

When faced with decisions that can affect multiple stakeholders, investment professionals must have a well-developed set of principles. Otherwise, their thought processes can lead to, at best, indecision and, at worst, fraudulent conduct and destruction of the public trust. Establishing an ethical framework to guide an investment professional’s internal thought process regarding how to act is a crucial step to engaging in ethical conduct.

Investment professionals are generally comfortable analyzing and making decisions from an economic (profit/loss) perspective. Given the importance of ethical behavior in carrying out professional responsibilities, it is also important to analyze decisions and their potential consequences from an ethical perspective. Using a framework for ethical decision-making will help investment professionals to effectively examine their choices in the context of conflicting interests common to their professional obligations (e.g., researching and gathering information, developing investment recommendations, and managing money for others). Such a framework provides investment professionals with a tool to help them adhere to a code of ethics and allows them to analyze and choose options to meet high standards of ethical behavior. By applying the framework and analyzing the particular circumstances of each available alternative, investment professionals are able to determine the best course of action to fulfill their responsibilities in an ethical manner.

An ethical decision-making framework helps a decision maker see the situation from multiple perspectives and pay attention to aspects of the situation that may be less evident with a short-term, self-focused perspective. The goal of getting a broader picture of a situation is to be able to create a plan of action that is less likely to harm stakeholders and more likely to benefit them. If a decision maker does not know or understand the effects of her or his actions on stakeholders, the likelihood of making a decision and taking action that harms stakeholders is more likely to occur, even if unintentionally. Finally, an ethical decisionmaking framework helps decision makers explain and justify their actions to a broader audience of stakeholders.

An ethical decision-making framework is designed to facilitate the decision-making process for all decisions. It helps people look at and evaluate a decision from multiple perspectives to identify important issues they might not otherwise consider. Using an ethical decision-making framework consistently helps develop sound judgment and decision-making skills and avoid making decisions that have unanticipated ethical consequences. Ethical decision-making frameworks come in many forms with varying degrees of detail. A general ethical decision-making framework is shown in Exhibit 1 .

投资组合代考

金融代写|投资组合代写Investment Portfolio代考|EXPECTATIONS OF INVESTMENT PROFESSIONALS

因此，CFA 持证人和其他金融行业参与者面临的问题不是在专业价值和商业价值之间做出选择。相反，它以一种将消费者和客户的最大利益置于我们自己的公司和个人利益之上的方式来平衡这对不断竞争的对。

所有投资专业人士…… 最好……培养对我们金融体系“大局”的敏锐意识，深刻反思我们如何才能使它变得更好，了解金融的悠久历史，并深入参与在我们的职业中培养如果我们要在未来几年内有效、诚实和审慎地为投资者服务，就必须具备最高品格。
-John C. Bogle，“平衡专业价值和商业价值”（2017 年）
对所有专业人士的期望特征和行为包括诚实、正直、利他主义、持续改进、卓越、忠诚以及对同事、雇主和客户的尊重。期望极高的标准，但不是完美的，包括在公共场合的行为。专业人士应通过自己的行动维护其专业声誉，并在工作中负责任、负责和可靠。专业人士应定期反思自我提升的周期，从自我评估开始，识别知识差距，编制持续专业发展计划以填补这些差距，将新的学习付诸实践，然后按顺序评估结果通知下一个周期。专业人士的主要职责是提供独立的建议，

预期的行为会因职业而异，但某些属性是大多数职业共有的。其中包括在交易中保持诚实和开放的义务，这涵盖了专业实践的所有方面，从撰写简历、提供建议、保存记录以及尽可能获得客户的知情同意。这意味着确保客户了解决策的后果、结果的范围和风险。客户不会拥有专业人士所具备的知识水平，但专业人士不应滥用这种更专业的知识。有时会因判断和实践的错误而对客户产生不利的后果。专业人员应准备好及时承认错误，从中吸取教训并纠正错误，包括让客户完整，在适当情况下。一些专业守则规定，专业人士必须与任何违反法律或法规的行为脱钩。这意味着即使个人无法改变他人的行为，他们也可能需要采取自己的行动来应对担忧。

金融代写|投资组合代写Investment Portfolio代考|Description of the Framework

当面临可能影响多个利益相关者的决策时，投资专业人士必须有一套完善的原则。否则，他们的思维过程最多可能导致优柔寡断，最坏的情况是欺诈行为和破坏公众信任。建立一个道德框架来指导投资专业人士关于如何行动的内部思考过程是从事道德行为的关键步骤。

投资专业人士通常乐于从经济（利润/损失）的角度进行分析和决策。鉴于道德行为在履行职业责任中的重要性，从道德角度分析决策及其潜在后果也很重要。使用道德决策框架将有助于投资专业人士在与其职业义务（例如，研究和收集信息、制定投资建议和为他人管理资金）共同利益冲突的背景下有效地审查他们的选择。这样的框架为投资专业人士提供了一种工具，帮助他们遵守道德准则，并允许他们分析和选择符合高标准道德行为的选项。

道德决策框架可帮助决策者从多个角度看待情况，并以短期、以自我为中心的视角关注情况可能不太明显的方面。更广泛地了解情况的目标是能够制定一个不太可能伤害利益相关者而更有可能使他们受益的行动计划。如果决策者不知道或不了解她或他的行为对利益相关者的影响，即使是无意的，也更有可能发生做出伤害利益相关者的决定和采取行动的可能性。最后，道德决策框架可以帮助决策者向更广泛的利益相关者解释和证明他们的行为。

道德决策框架旨在促进所有决策的决策过程。它可以帮助人们从多个角度看待和评估决策，以识别他们可能不会考虑的重要问题。始终如一地使用道德决策框架有助于培养正确的判断和决策技能，并避免做出具有未预料到的道德后果的决策。道德决策框架有多种形式，具有不同程度的细节。图表 1 显示了一个通用的道德决策框架。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考

随机过程代考

贝叶斯方法代考

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

机器学习代写

多元统计分析代考

基础数据: $N$ 个样本， $P$ 个变量数的单样本，组成的横列的数据表
变量定性: 分类和顺序；变量定量：数值
数学公式的角度分为: 因变量与自变量

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

金融代写|投资组合代写Investment Portfolio代考|FINC3017

Posted on 2022年8月29日2022年8月29日 by statistics-lab

如果你也在怎样代写投资组合Investment Portfolio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。

投资组合是由投资人或金融机构所持有的股票、债券、金融衍生产品等组成的集合。目的是分散风险。投资组合可以看成几个层面上的组合。

我们提供的投资组合Investment Portfolio及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等楖率论
Advanced Mathematical Statistics 高等数理统计学
(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

金融代写|投资组合代写Investment Portfolio代考|FINC3017

金融代写|投资组合代写Investment Portfolio代考|Trust in the Investment Industry

The investment professional today has similarities with professionals in longer-established professions, such as medicine and law. Like doctors and lawyers, investment professionals are trusted to draw on a body of formal knowledge and apply that knowledge with care and judgment. In comparison to clients, investment professionals are also expected to have superior financial expertise, technical knowledge, and knowledge of the applicable laws and regulations. There is a risk that clients may not be fully aware of the conflicts, risks, and fees involved, so investment professionals must always handle and fully disclose these issues in a way that serves the best interests of clients. Compliance with codes of ethics and professional standards is essential, and practice must be guided by care, transparency, and integrity.
The investment profession and investment firms must be interdependent to foster trust. Employers and regulators have their own standards and practices that may differ from regulations and standards set by professional bodies. The investment professional bodies typically direct professionals in how to resolve these differences.

In many countries, the investment profession affects many key aspects of the economy, including savings, retirement planning, and the pricing and allocation of capital. In most countries, skilled evaluation of securities leads to more efficient capital allocation and, combined with ethical corporate governance, can assist in attracting investment from international investors. The investment profession can deliver more value to society when higher levels of trust and better capital allocation reduce transaction costs and help meet client objectives. These reasons explain why practitioners, clients, regulators, and governments have supported the development of an investment management profession.

金融代写|投资组合代写Investment Portfolio代考|CFA Institute as an Investment Professional Body

CFA Institute is the largest global association of investment professionals. Created in the early $1960 \mathrm{~s}$, it moved beyond North America in the $1980 \mathrm{~s}$, reflecting the globalization of investment management. CFA Institute initiated a number of other changes in line with the growth of investment management. One significant change occurred in 2015, when CFA Institute decided to implement the highest standards of governance in the US not-for-profit sector. The Board of Governors resolved “to implement US Public Company Standards and US not-for-profit leading practices, unless the Board determines that it is not in the best interest of the membership or organization to do so.”

The mission of CFA Institute is “to lead the investment profession globally, by promoting the highest standards of ethics, education, and professional excellence for the ultimate benefit of society.” The CFA Institute Code of Ethics and Standards of Professional Conduct (Code and Standards) promote the integrity of members ${ }^{2}$ and establish a model for ethical behavior. CFA Institute candidates and members must meet the highest standards among those established by CFA Institute, regulators, or the employer. Where client interests and market interests conflict, the Code and Standards set an investment professional’s duty to market integrity as the overriding obligation. The advocacy efforts of CFA Institute aim to build market integrity by calling for regulations that align the interests of firms and clients.
As a professional body, CFA Institute gathers knowledge from practicing investment professionals, develops high-quality curricula, conducts rigorous examinations, and ensures practitioner involvement in developing its codes and values. Through interactions with practicing investment professionals, CFA Institute has developed a body of knowledge for the investment profession. This body of knowledge is updated on an ongoing basis through a process known as practice analysis. Practice analysis helps ensure that the CFA Institute Global Body of Investment Knowledge (GBIK) and the CFA Program Candidates Body of Knowledge (CBOK) remain current and globally relevant. The CFA Program CBOK focuses on the core knowledge, skills, and abilities (competencies) that are generally accepted and applied by investment professionals. These competencies are used in practice in a generalist context and are expected to be demonstrated by a recently qualified CFA charterholder.

投资组合代考

金融代写|投资组合代写Investment Portfolio代考|Trust in the Investment Industry

今天的投资专业人士与医学和法律等历史悠久的专业人士有相似之处。与医生和律师一样，投资专业人士被信任能够利用大量的正式知识，并谨慎和判断地应用这些知识。与客户相比，投资专业人士还应具备卓越的金融专业知识、技术知识以及适用法律法规的知识。客户可能无法完全意识到所涉及的冲突、风险和费用，因此投资专业人士必须始终以符合客户最大利益的方式处理和充分披露这些问题。遵守道德规范和专业标准至关重要，实践必须以谨慎、透明和正直为指导。
投资专业和投资公司必须相互依存以促进信任。雇主和监管机构有自己的标准和做法，可能不同于专业机构制定的法规和标准。投资专业机构通常指导专业人士如何解决这些差异。

在许多国家，投资行业影响经济的许多关键方面，包括储蓄、退休计划以及资本的定价和分配。在大多数国家，对证券进行熟练的评估可以提高资本配置的效率，再加上合乎道德的公司治理，有助于吸引国际投资者的投资。当更高水平的信任和更好的资本配置降低交易成本并帮助实现客户目标时，投资专业可以为社会创造更多价值。这些原因解释了为什么从业者、客户、监管机构和政府支持投资管理专业的发展。

金融代写|投资组合代写Investment Portfolio代考|CFA Institute as an Investment Professional Body

CFA Institute 是全球最大的投资专业人士协会。创建于早期1960 s，它在1980 s，体现了投资管理的全球化。随着投资管理的发展，CFA Institute 发起了许多其他变革。2015 年发生了一项重大变化，当时 CFA 协会决定在美国非营利部门实施最高标准的治理。理事会决定“实施美国上市公司标准和美国非营利领先实践，除非理事会确定这样做不符合会员或组织的最佳利益。”

CFA Institute 的使命是“通过促进最高标准的道德、教育和专业卓越，以最终造福社会，引领全球投资行业”。CFA Institute Code of Ethics and Standards of Professional操守（Code and Standards）促进会员诚信2并建立道德行为模式。CFA 协会的候选人和会员必须达到 CFA 协会、监管机构或雇主制定的最高标准。当客户利益与市场利益发生冲突时，守则和标准将投资专业人士的市场诚信义务设定为首要义务。CFA 协会的倡导工作旨在通过制定符合公司和客户利益的法规来建立市场诚信。
作为一个专业机构，CFA Institute 从执业投资专业人士那里收集知识，开发高质量的课程，进行严格的考试，并确保从业者参与制定其准则和价值观。通过与执业投资专业人士的互动，CFA 协会为投资专业发展了一套知识体系。这一知识体系通过称为实践分析的过程不断更新。实践分析有助于确保 CFA 学院全球投资知识体系 (GBIK) 和 CFA 计划候选人知识体系 (CBOK) 保持最新和全球相关性。CFA Program CBOK侧重于投资专业人士普遍接受和应用的核心知识、技能和能力（能力）。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考

随机过程代考

贝叶斯方法代考

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

机器学习代写

多元统计分析代考

基础数据: $N$ 个样本， $P$ 个变量数的单样本，组成的横列的数据表
变量定性: 分类和顺序；变量定量：数值
数学公式的角度分为: 因变量与自变量

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

金融代写|金融微积分代写Finance Calculus代考|MATH294

Posted on 2022年8月29日2022年8月29日 by statistics-lab

如果你也在怎样代写金融微积分Finance Calculus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。

在量化金融中，该理论被称为伊藤微积分。随机微积分在金融领域的主要用途是通过对布莱克-斯科尔斯模型中的资产价格的随机运动进行建模。

statistics-lab™ 为您的留学生涯保驾护航在代写金融微积分Finance Calculus方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写金融微积分Finance Calculus方面经验极为丰富，各种代写金融微积分Finance Calculus相关的作业也就用不着说。

我们提供的金融微积分Finance Calculus及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等楖率论
Advanced Mathematical Statistics 高等数理统计学
(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

金融代写|金融微积分代写Finance Calculus代考|Markov Property

The Markov Property of a Standard Wiener Process. Let $(\Omega, \mathscr{F}, \mathbb{P})$ be a probability space and let $\left{W_{t}: t \geq 0\right}$ be a standard Wiener process with respect to the filtration $\mathscr{F}{t}, t \geq 0$. Show that if $f$ is a continuous function then there exists another continuous function $g$ such that $$ \mathbb{E}\left[f\left(W{t}\right) \mid \mathscr{F}{u}\right]=g\left(W{u}\right)
$$
for $0 \leq u \leq t$
Solution: For $0 \leq u \leq t$ we can write
$$
\mathbb{E}\left[f\left(W_{t}\right) \mid \mathscr{F}{u}\right]=\mathbb{E}\left[f\left(W{t}-W_{u}+W_{u}\right) \mid \mathscr{F}{u}\right] . $$ Since $W{t}-W_{u} \Perp \mathscr{F}{u}$ and $W{u}$ is $\mathscr{F}{u}$ measurable, by setting $W{u}=x$ where $x$ is a constant value
$$
\mathbb{E}\left[f\left(W_{t}-W_{u}+W_{u}\right) \mid \mathscr{F}{u}\right]=\mathbb{E}\left[f\left(W{t}-W_{u}+x\right)\right] .
$$
Because $W_{t}-W_{u} \sim \mathcal{N}(0, t-u)$ we can write $\mathbb{E}\left[f\left(W_{t}-W_{u}+x\right)\right]$ as
$$
\mathrm{E}\left[f\left(W_{t}-W_{u}+x\right)\right]=\frac{1}{\sqrt{2 \pi(t-u)}} \int_{-\infty}^{\infty} f(w+x) e^{-\frac{u^{2}}{2(t-u)}} d w
$$ By setting $\tau=t-u$ and $y=w+x$, we can rewrite $\mathbb{E}\left[f\left(W_{t}-W_{u}+x\right)\right]=\mathbb{E}\left[f\left(W_{t}-W_{u}+\right.\right.$ $\left.\left.W_{u}\right)\right]$ as
$$
\begin{aligned}
\mathrm{E}\left[f\left(W_{t}-W_{u}+W_{u}\right)\right] &=\frac{1}{\sqrt{2 \pi \tau}} \int_{-\infty}^{\infty} f(y) e^{-\frac{(y-x)^{2}}{y r}} d y \
&=\int_{-\infty}^{\infty} f(y) p\left(\tau, W_{u}, y\right) d y
\end{aligned}
$$
where the transition density
$$
p\left(\tau, W_{u}, y\right)=\frac{1}{\sqrt{2 \pi \tau}} e^{-\frac{\left(\gamma-W_{u}\right)^{2}}{2 \mathrm{r}}}
$$
is the density of $Y \sim \mathcal{N}\left(W_{u}, \tau\right)$. Since the only information from the filtration $\mathscr{F}{u}$ is $W{u}$, therefore
$$
\mathrm{E}\left[f\left(W_{t}\right) \mid \mathscr{F}{u}\right]=g\left(W{u}\right)
$$
where
$$
g\left(W_{u}\right)=\int_{-\infty}^{\infty} f(y) p\left(\tau, W_{u}, y\right) d y .
$$

金融代写|金融微积分代写Finance Calculus代考|Martingale Property

Let $(\Omega, \mathscr{F}, \mathbb{P})$ be a probability space and let $\left{W_{t}: t \geq 0\right}$ be a standard Wiener process. Show that $W_{t}$ is a martingale.
Solution: Given $W_{t} \sim \mathscr{N}(0, t)$.

(a) For $s \leq t$ and since $W_{t}-W_{s} \Perp \mathscr{F}{s}$, we have $$ \mathbb{E}\left(W{t} \mid \mathscr{F}{s}\right)=\mathbb{E}\left(W{t}-W_{s}+W_{s} \mid \mathscr{F}{s}\right)=\mathbb{E}\left(W{t}-W_{s} \mid \mathscr{F}{s}\right)+\mathbb{E}\left(W{s} \mid \mathscr{F}{s}\right)=W{s} .
$$
(b) Since $W_{t} \sim \mathcal{N}(0, t),\left|W_{t}\right|$ follows a folded normal distribution such that $\left|W_{t}\right| \sim$ $\mathscr{N}{f}(0, t)$. From Problem 1.2.2.11 (page 22), we can deduce $\mathbb{E}\left(\left|W{t}\right|\right)=\sqrt{2 t / \pi}<\infty$. In contrast, we can also utilise Hölder’s inequality (see Problem 1.2.3.2, page 41) to deduce that $\mathbb{E}\left(\left|W_{t}\right|\right) \leq \sqrt{\mathbb{E}\left(W_{t}^{2}\right)}=\sqrt{t}<\infty$.
(c) $W_{t}$ is clearly $\mathscr{F}{t}$-adapted. From the results of (a) $-(\mathrm{c})$ we have shown that $W{t}$ is a martingale.

Let $(\Omega, \mathscr{F}, \mathbb{P})$ be a probability space and let $\left{W_{t}: t \geq 0\right}$ be a standard Wiener process. Show that $X_{t}=W_{t}^{2}-t$ is a martingale.
Solution: Given $W_{t} \sim \mathscr{N}(0, t)$
(a) For $s \leq t$ and since $W_{t}-W_{s} \mathbb{\Perp} \mathscr{F}{s}$. we have $$ \begin{aligned} \mathbb{E}\left(W{t}^{2}-t \mid \mathscr{F}{s}\right) &=\mathbb{E}\left[\left(W{t}-W_{s}+W_{s}\right)^{2} \mid \mathscr{F}{s}\right]-t \ &=\mathbb{E}\left[\left(W{t}-W_{s}\right)^{2} \mid \mathscr{F}{s}\right]+2 \mathbb{E}\left[W{s}\left(W_{t}-W_{s}\right) \mid \mathscr{F}{s}\right]+\mathbb{E}\left(W{s}^{2} \mid \mathscr{F}{s}\right)-t \ &=t-s+0+W{s}^{2}-t \
&=W_{s}^{2}-s .
\end{aligned}
$$
(b) Since $\left|X_{t}\right|=\left|W_{t}^{2}-t\right| \leq W_{t}^{2}+t$ we can therefore write
$$
\mathbb{E}\left(\left|W_{t}^{2}-t\right|\right) \leq \mathbb{E}\left(W_{t}^{2}+t\right)=\mathbb{E}\left(W_{t}^{2}\right)+t=2 t<\infty .
$$
(c) Since $X_{t}=W_{t}^{2}-t$ is a function of $W_{t}$, hence it is $\mathscr{F}{t}$-adapted. From the results of $(\bar{a})-(c)$ we have shown that $X{t}=W_{t}^{2}-t$ is a martingale.

金融微积分代考

金融代写|金融微积分代写Finance Calculus代考|Markov Property

标准维纳过程的马尔可夫性质。让 $(\Omega, \mathscr{F}, \mathbb{P})$ 是一个概率空间，论 lleft{W_{t}: t lgeq O\right } 是关于过滤的标准维纳过程 $\mathscr{F} t, t \geq 0$. 证明如果 $f$ 是一个连续函数，那么存在另一个连续函数 $g$ 这样 $\$ \$$
$\$ \$$
为了 $0 \leq u \leq t$
解决方案: 对于 $0 \leq u \leq t$ 我们可以写
$\$ \$$ $\mathrm{W}{-}{u}+\mathrm{W}{-}{u} \backslash$ right) $\backslash$ Imid $\backslash$ mathscr{F}u} $\backslash$ right] 。 $\$ \$$ 因为 $W t-W_{u} \backslash$ Perp $\mathscr{F} u$ 和 $W u$ 是 $\mathscr{F} u$ 可测量的，通过设置 $W u=x$ 在哪里 $x$ 是一个常数值
$\$ \$$ $\mathrm{W}_{-}{u}+x \backslash$ right)\right]
$\$ \$$
因为 $W_{t}-W_{u} \sim \mathcal{N}(0, t-u)$ 我们可以写 $\mathbb{E}\left[f\left(W_{t}-W_{u}+x\right)\right]$ 作为
$\$ \$$ $\mathrm{e}^{\wedge}{-\operatorname{Ifrac}{\mathrm{u} \wedge{2}}{2(\mathrm{tu})}} \mathrm{dw}$
\$\$通过设置 $\tau=t-u$ 和 $y=w+x$, 我们可以重写 $\mathbb{E}\left[f\left(W_{t}-W_{u}+x\right)\right]=\mathbb{E}\left[f\left(W_{t}-W_{u}+W_{u}\right)\right]$ 作为
$\$ \$$
开始{对齐}
$f(y) e^{\wedge}\left{-\backslash f r a c\left{(y x)^{\wedge}{2}\right}{y r}\right} d y \backslash$
lend{对齐}
$\$ \$$
其中过渡密度

金融代写|金融微积分代写Finance Calculus代考|Martingale Property

解决方案：给定 $W_{t} \sim \mathscr{N}(0, t)$.
(a) 为 $s \leq t$ 并且因为 $W_{t}-W_{s} \backslash \operatorname{Perp} \mathscr{F} s$ ，我们有
$$
\mathbb{E}\left(W t \mid \mathscr{F}{s}\right)=\mathbb{E}\left(W t-W{s}+W_{s} \mid \mathscr{F}{s}\right)=\mathbb{E}\left(W t-W{s} \mid \mathscr{F}{s}\right)+\mathbb{E}\left(W s \mid \mathscr{F}{s}\right)=W s .
$$
(b) 由于 $W_{t} \sim \mathcal{N}(0, t),\left|W_{t}\right|$ 遵循折餷正态分布，使得 $\left|W_{t}\right| \sim \mathscr{N} f(0, t)$. 从问题 1.2.2.11 (第 22 页)，我们可以推导出 $\mathbb{E}(|W t|)=\sqrt{2 t / \pi}<\infty$. 相反，我们还可以利用 Hölder 不等式（参见第 41 页的问题 1.2.3.2) 来推导出 $\mathbb{E}\left(\left|W_{t}\right|\right) \leq \sqrt{\mathbb{E}\left(W_{t}^{2}\right)}=\sqrt{t}<\infty$.
(C) $W_{t}$ 显然是 $\mathscr{F} t$-适应。根据 (a) 的结果 $-(\mathrm{c})$ 我们已经证明 $W t$ 是鞅。
让 $(\Omega, \mathscr{F}, \mathbb{P})$ 是一个概率空间，论 lleft{W_{t}: t lgeq Olright } 是一个标准的维纳过程。显示 $X_{t}=W_{t}^{2}-t$ 是鞅。
解决方案: 给定 $W_{t} \sim \mathscr{N}(0, t)$
(a) 为 $s \leq t$ 并且因为 $W_{t}-W_{s} \backslash \operatorname{Perp} \mathscr{F} s$. 我们有
$$
\mathbb{E}\left(W t^{2}-t \mid \mathscr{F}{s}\right)=\mathbb{E}\left[\left(W t-W{s}+W_{s}\right)^{2} \mid \mathscr{F}{s}\right]-t \quad=\mathbb{E}\left[\left(W t-W{s}\right)^{2} \mid \mathscr{F}{s}\right]+2 \mathbb{E}[W s $$ (b) 由于 $\left|X{t}\right|=\left|W_{t}^{2}-t\right| \leq W_{t}^{2}+t$ 因此我们可以写
$$
\mathbb{E}\left(\left|W_{t}^{2}-t\right|\right) \leq \mathbb{E}\left(W_{t}^{2}+t\right)=\mathbb{E}\left(W_{t}^{2}\right)+t=2 t<\infty .
$$
(c) 由于 $X_{t}=W_{t}^{2}-t$ 是一个函数 $W_{t}$ ，因此它是 $\mathscr{F} t$-适应。从结果来看 $(\bar{a})-(c)$ 我们已经证明 $X t=W_{t}^{2}-t$ 是鞅。

金融代写|金融微积分代写Finance Calculus代考请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考

随机过程代考

贝叶斯方法代考

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

机器学习代写

多元统计分析代考

基础数据: $N$ 个样本， $P$ 个变量数的单样本，组成的横列的数据表
变量定性: 分类和顺序；变量定量：数值
数学公式的角度分为: 因变量与自变量

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

金融代写|金融微积分代写Finance Calculus代考|MATH681

Posted on 2022年8月29日2022年8月29日 by statistics-lab

如果你也在怎样代写金融微积分Finance Calculus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。

在量化金融中，该理论被称为伊藤微积分。随机微积分在金融领域的主要用途是通过对布莱克-斯科尔斯模型中的资产价格的随机运动进行建模。

我们提供的金融微积分Finance Calculus及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等楖率论
Advanced Mathematical Statistics 高等数理统计学
(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

金融代写|金融微积分代写Finance Calculus代考|Properties of Expectations

Show that if $X$ is a random variable taking non-negative values then $\mathbb{E}(X)= \begin{cases}\sum_{x=0}^{\infty} \mathbb{P}(X>x) & \text { if } X \text { is a discrcte random variable } \ \int_{0}^{\infty} \mathbb{P}(X \geq x) d x & \text { if } X \text { is a continuous random variable. }\end{cases}$
Solution: We first show the result when $X$ takes non-negative integer values only. By definition
$$
\begin{aligned}
\mathbb{E}(X) &=\sum_{y=0}^{\infty} y \mathbb{P}(X=y) \
&=\sum_{y=0}^{\infty} \sum_{x=0}^{y} \mathbb{P}(X=y) \
&=\sum_{x=0}^{\infty} \sum_{y=x+1}^{\infty} \mathbb{P}(X=y) \
&=\sum_{x=0}^{\infty} \mathbb{P}(X>x)
\end{aligned}
$$
For the case when $X$ is a continuous random variable taking non-negative values we have
$$
\begin{aligned}
\mathbb{E}(X) &=\int_{0}^{\infty} y f_{X}(y) d y \
&=\int_{0}^{\infty}\left{\int_{0}^{y} f_{X}(y) d x\right} d y \
&=\int_{0}^{\infty}\left{\int_{x}^{\infty} f_{X}(y) d y\right} d x \
&=\int_{0}^{\infty} \mathbb{P}(X \geq x) d x .
\end{aligned}
$$
Hölder’s Inequality. Let $\alpha, \beta \geq 0$ and for $p, q>1$ such that $\frac{1}{p}+\frac{1}{q}=1$ show that the following inequality:
$$
\alpha \beta \leq \frac{\alpha^{p}}{p}+\frac{\beta^{q}}{q}
$$ holds. Finally, if $X$ and $Y$ are a pair of jointly continuous variables, show that
$$
\mathbb{E}(|X Y|) \leq\left{\mathbb{E}\left(\left|X^{p}\right|\right)\right}^{1 / p}\left{\mathbb{E}\left(\left|Y^{q}\right|\right)\right}^{1 / q} $$

金融代写|金融微积分代写Finance Calculus代考|Basic Properties

Let $(\Omega, \mathscr{F}, \mathbb{P})$ be a probability space. We consider a symmetric random walk such that the $j$-th step is defined as
$$
Z_{j}= \begin{cases}1 & \text { with probability } \frac{1}{2} \ -1 & \text { with probability } \frac{1}{2}\end{cases}
$$
where $Z_{i} \Perp Z_{j}, i \neq j$. By setting $0=k_{0}<k_{1}<k_{2}<\ldots<k_{t}$, we let
$$
M_{k_{i}}=\sum_{j=1}^{k_{i}} Z_{j}, \quad i=1,2, \ldots, t
$$
where $M_{0}=0$.
Show that the symmetric random walk has independent increments such that the random variables
$$
M_{k_{1}}-M_{k_{0}}, M_{k_{2}}-M_{k_{1}}, \ldots, M_{k_{t}}-M_{k_{t-1}}
$$
are independent.
Finally, show that $\mathbb{E}\left(M_{k_{i+1}}-M_{k_{i}}\right)=0$ and $\operatorname{Var}\left(M_{k_{i+1}}-M_{k_{i}}\right)=k_{i+1}-k_{i}$.
Solution: By definition
$$
M_{k_{i}}-M_{k_{i-1}}=\sum_{j=k_{i-1}+1}^{k_{i}} Z_{j}=s_{i}
$$
and for $m<n, m, n=1,2, \ldots, t$,
$$
\begin{aligned}
&\mathbb{P}\left(M_{k_{n}}-M_{k_{n-1}}=s_{n} \mid M_{k_{m}}-M_{k_{m-1}}=s_{m}\right) \
&=\frac{\mathbb{P}\left(M_{k_{n}}-M_{k_{n-1}}=s_{n}, M_{k_{m}}-M_{k_{m-1}}=s_{m}\right)}{\mathbb{P}\left(M_{k_{m}}-M_{k_{m-1}}=s_{m}\right)} \
&=\frac{\mathbb{P}\left(\text { sum of walks in }\left[k_{n-1}+1, k_{n}\right] \cap \text { sum of walks in }\left[k_{m-1}+1, k_{m}\right]\right)}{\mathbb{P}\left(\text { sum of walks in }\left[k_{m-1}+1, k_{m}\right]\right)} .
\end{aligned}
$$
Because $m<n$ and since $Z_{i}$ is independent of $Z_{j}, i \neq j$, there are no overlapping events between the intervals $\left[k_{n-1}+1, k_{n}\right]$ and $\left[k_{m-1}+1, k_{m}\right]$ and hence for $m<n$, $m, n=1,2, \ldots, t$,
$$
\begin{aligned}
\mathbb{P}\left(M_{k_{n}}-M_{k_{n-1}}=s_{n} \mid M_{k_{m}}-M_{k_{m-1}}=s_{m}\right) &=\mathbb{P}\left(\text { sum of walks in }\left[k_{n-1}+1, k_{n}\right]\right) \
&=\mathbb{P}\left(M_{k_{n}}-M_{k_{n-1}}=s_{n}\right) .
\end{aligned}
$$

金融微积分代考

金融代写|金融微积分代写Finance Calculus代考|Properties of Expectations

证明如果 $X$ 是一个随机变量，取非负值然后
$\mathbb{E}(X)=\left{\sum_{x=0}^{\infty} \mathbb{P}(X>x) \quad\right.$ if $X$ is a discrcte random variable $\int_{0}^{\infty} \mathbb{P}(X \geq x) d x \quad$ if $X$ is a co 解决方案: 我们先显示结果 $X$ 仅采用非负整数值。根据定义
$$
\mathbb{E}(X)=\sum_{y=0}^{\infty} y \mathbb{P}(X=y) \quad=\sum_{y=0}^{\infty} \sum_{x=0}^{y} \mathbb{P}(X=y)=\sum_{x=0}^{\infty} \sum_{y=x+1}^{\infty} \mathbb{P}(X=y) \quad=\sum_{x=0}^{\infty} \mathbb{P}(X>x)
$$
对于这种情况 $X$ 是一个连续随机变量，取非负值，我们有
持有人不等式。让 $\alpha, \beta \geq 0$ 并且对于 $p, q>1$ 这样 $\frac{1}{p}+\frac{1}{q}=1$ 证明以下不等式:
$$
\alpha \beta \leq \frac{\alpha^{p}}{p}+\frac{\beta^{q}}{q}
$$
持有。最后，如果 $X$ 和 $Y$ 是一对联合连续变量，证明

金融代写|金融微积分代写Finance Calculus代考|Basic Properties

让 $(\Omega, \mathscr{F}, \mathbb{P})$ 是一个概率空间。我们考虑一个对称的随机游走，使得 $j$-th 步定义为
$\$ \$$
Z-{j}=
$\left{1\right.$ with probability $\frac{1}{2}-1 \quad$ with probability $\frac{1}{2}$
$\$ \$$
在哪里 $Z_{i} \backslash \operatorname{Perp} Z_{j}, i \neq j$. 通过设置 $0=k_{0}<k_{1}<k_{2}<\ldots<k_{t}$ ，我们让
$\$ \$$
$M_{-}\left{k_{-}{i}\right}=\backslash$ sum_ ${j=1} \wedge\left{k_{-}{i}\right} Z_{-}{j}$, lquad $i=1,2, \vee d o t s, t$
$\$ \$$
在哪里 $M_{0}=0$.
证明对称随机游走具有独立增量，使得随机变量
$\$ \$$
$M_{-}\left{k_{-}{1}\right}-M_{-}\left{k_{-}{0}\right}, M_{-}\left{k_{-}{2}\right}-M_{-}\left{k_{-}{1}\right}, \backslash / d o t s^{\prime} M_{-}\left{k_{-}{t}\right}-M_{{}\left{k_{-}{t-1}\right}$
\$\$
是独立的。
最后，证明 $\mathbb{E}\left(M_{k_{i+1}}-M_{k_{i}}\right)=0$ 和 $\operatorname{Var}\left(M_{k_{i+1}}-M_{k_{i}}\right)=k_{i+1}-k_{i}$.
解决方案：根据定义
$\$ \$$
$M_{-}\left{k_{-}{i}\right}-M_{-}\left{k_{-}{i-1}\right}=\backslash$ sum_{j=k_{i-1 $\left.}+1\right} \wedge\left{k_{-}{i}\right} Z_{-}{j}=s_{-}{i}$
$\$ \$$
并且对于 $m<n, m, n=1,2, \ldots, t$,

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考

随机过程代考

贝叶斯方法代考

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

机器学习代写

多元统计分析代考

基础数据: $N$ 个样本， $P$ 个变量数的单样本，组成的横列的数据表
变量定性: 分类和顺序；变量定量：数值
数学公式的角度分为: 因变量与自变量

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

金融代写|金融微积分代写Finance Calculus代考|MATH205

Posted on 2022年8月29日2022年8月29日 by statistics-lab

如果你也在怎样代写金融微积分Finance Calculus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。

在量化金融中，该理论被称为伊藤微积分。随机微积分在金融领域的主要用途是通过对布莱克-斯科尔斯模型中的资产价格的随机运动进行建模。

我们提供的金融微积分Finance Calculus及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等楖率论
Advanced Mathematical Statistics 高等数理统计学
(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

金融代写|金融微积分代写Finance Calculus代考|Probability Spaces

De Morgan’s Law. Let $A_{i}, i \in I$ where $I$ is some, possibly uncountable, indexing set. Show that
(a) $\left(\bigcup_{i \in I} A_{i}\right)^{c}=\bigcap_{i \in I} A_{i}^{c}$.
(b) $\left(\bigcap_{i \in I} A_{i}\right)^{c}=\bigcup_{i \in I} A_{i}^{c}$.
Solution:
(a) Let $a \in\left(\bigcup_{i \in I} A_{i}\right)^{c}$ which implies $a \notin \bigcup_{i \in I} A_{i}$, so that $a \in A_{i}^{c}$ for all $i \in I$. Therefore,
$$
\left(\bigcup_{i \in I} A_{i}\right)^{c} \subseteq \bigcap_{i \in I} A_{i}^{c} .
$$
On the contrary, if we let $a \in \bigcap_{i \in I} A_{i}^{c}$ then $a \notin A_{t}$ for all $i \in I$ or $a \in\left(\bigcup_{i \in I} A_{t}\right)^{c}$ and hence
$$
\bigcap_{i \in I} A_{i}^{c} \subseteq\left(\bigcup_{i \in I} A_{i}\right)^{c} \text {. }
$$
Therefore, $\left(\bigcup_{i c I} A_{i}\right)^{c}=\bigcap_{i c I} A_{i}^{c}$.
(b) From (a), we can write
$$
\left(\bigcup_{i \in I} A_{i}^{c}\right)^{c}=\bigcap_{i \in I}\left(A_{i}^{c}\right)^{c}=\bigcap_{i \in I} A_{i}
$$
Taking complements on both sides gives
$$
\left(\bigcap_{i \in I} A_{i}\right)^{c}=\bigcup_{i \in I} A_{i}^{r}
$$
Let $\mathscr{F}$ be a $\sigma$-algebra of subsets of the sample space $\Omega$. Show that if $A_{1}, A_{2}, \ldots \in \mathscr{F}$ then $\bigcap_{i=1}^{\infty} A_{i} \in \mathscr{F}_{\text {. }}$.

Solution: Given that $\mathscr{F}$ is a $\sigma$-algebra then $A_{1}^{c}, A_{2}^{c}, \ldots \in \mathscr{F}$ and $\bigcup_{i=1}^{\infty} A_{i}^{c} \in \mathscr{F}$. Furthermore, the complement of $\bigcup_{i-1}^{\infty} A_{i}^{c}$ is $\left(\bigcup_{i-1}^{\infty} A_{i}^{c}\right)^{c} \in \mathscr{F}$.

Thus, from De Morgan’s law (see Problem 1.2.1.1, page 4) we have $\left(\bigcup_{i=1}^{\infty} A_{i}^{c}\right)^{c}=$ $\bigcap_{i=1}^{\infty}\left(A_{i}^{c}\right)^{c}=\bigcap_{i=1}^{\infty} A_{i} \in \mathscr{F}$

金融代写|金融微积分代写Finance Calculus代考|Discrete and Continuous Random Variables

Bernoulli Distribution. Let $X$ be a Bernoulli random variable, $X \sim \operatorname{Bernoulli}(p), p \in[0,1]$ with probability mass function
$$
\mathbb{P}(X=1)=p, \quad \mathbb{P}(X=0)=1-p .
$$
Show that $\mathrm{E}(X)=p$ and $\operatorname{Var}(X)=p(1-p)$.
Solution: If $X \sim \operatorname{Bernoulli}(p)$ then we can write
$$
\mathbb{P}(X=x)=p^{x}(1-p)^{1-x}, \quad x \in{0,1}
$$
and by definition
$$
\begin{gathered}
\mathbb{E}(X)=\sum_{x=0}^{1} x \mathbb{P}(X=x)=0 \cdot(1-p)+1 \cdot p=p \
\mathbb{E}\left(X^{2}\right)=\sum_{x=0}^{1} x^{2} \mathbb{P}(X=x)=0 \cdot(1-p)+1 \cdot p=p
\end{gathered}
$$
and hence
$$
\mathbb{F}(X)=p, \quad \operatorname{Var}(X)=\mathbb{F}\left(X^{2}\right)-\mathbb{F}(X)^{2}=p(1-p)
$$
Binomial Distribution. Let $\left{X_{i}\right}_{i=1}^{n}$ be a sequence of independent Bernoulli random variables each with probability mass function
$$
\mathbb{P}\left(X_{i}=1\right)=p, \quad \mathbb{P}\left(X_{i}=0\right)=1-p, \quad p \in[0,1]
$$
and let
$$
X=\sum_{i=1}^{n} X_{i} .
$$
Show that $X$ follows a binomial distribution, $X \sim \operatorname{Binomial}(n, p)$ with probability mass function
$$
\mathbb{P}(X=k)=\left(\begin{array}{l}
n \
k
\end{array}\right) p^{k}(1-p)^{n-k}, \quad k=0,1,2, \ldots, n
$$
such that $\mathrm{E}(X)=n p$ and $\operatorname{Var}(X)=n p(1-p)$.
Using the central limit theorem show that $X$ is approximately normally distributed, $X \div$ $\mathcal{N}(n p, n p(1-p))$ as $n \rightarrow \infty$.

金融微积分代考

金融代写|金融微积分代写Finance Calculus代考|Probability Spaces

来自摩根定律。晩的 $A_{i}, i \in I$ 在哪里 $I$ 是一些可能不可数的索引集。证明
(a) $\left(\bigcup_{i \in I} A_{i}\right)^{c}=\bigcap_{i \in I} A_{i}^{c}$.
(二) $\left(\bigcap_{i \in I} A_{i}\right)^{c}=\bigcup_{i \in I} A_{i}^{c}$.
解决方案：
(a) 让 $a \in\left(\bigcup_{i \in I} A_{i}\right)^{c}$ 这意味着 $a \notin \bigcup_{i \in I} A_{i}$ ，以便 $a \in A_{i}^{c}$ 对所有人 $i \in I$. 所以，
$$
\left(\bigcup_{i \in I} A_{i}\right)^{c} \subseteq \bigcap_{i \in I} A_{i}^{c} .
$$
相反，如果我们让 $a \in \bigcap_{i \in I} A_{i}^{c}$ 然后 $a \notin A_{t}$ 对所有人 $i \in I$ 或者 $a \in\left(\bigcup_{i \in I} A_{t}\right)^{c}$ 因此
$$
\bigcap_{i \in I} A_{i}^{c} \subseteq\left(\bigcup_{i \in I} A_{i}\right)^{c} .
$$
所以， $\left(\bigcup_{i c I} A_{i}\right)^{c}=\bigcap_{i c I} A_{i}^{c}$.
(b) 从 (a)，我们可以写出
$$
\left(\bigcup_{i \in I} A_{i}^{c}\right)^{c}=\bigcap_{i \in I}\left(A_{i}^{c}\right)^{c}=\bigcap_{i \in I} A_{i}
$$
两边取补码
$$
\left(\bigcap_{i \in I} A_{i}\right)^{c}=\bigcup_{i \in I} A_{i}^{r}
$$
让 $\mathscr{F}$ 做一个 $\sigma$ – 样本空间子集的代数 $\Omega$. 证明如果 $A_{1}, A_{2}, \ldots \in \mathscr{F}$ 然后 $\bigcap_{i=1}^{\infty} A_{i} \in \mathscr{F} .$.
解决方案: 鉴于 $\mathscr{F}$ 是一个 $\sigma$-然后代数 $A_{1}^{c}, A_{2}^{c}, \ldots \in \mathscr{F}$ 和 $\bigcup_{i=1}^{\infty} A_{i}^{c} \in \mathscr{F}$. 此外，补 $\bigcup_{i-1}^{\infty} A_{i}^{c}$ 是 $\left(\bigcup_{i-1}^{\infty} A_{i}^{c}\right)^{c} \in \mathscr{F}$
因此，根据德摩根定律 (参见问题 1.2.1.1，第 4 页)，我们有 $\left(\bigcup_{i=1}^{\infty} A_{i}^{c}\right)^{c}=\bigcap_{i=1}^{\infty}\left(A_{i}^{c}\right)^{c}=\bigcap_{i=1}^{\infty} A_{i} \in \mathscr{F}$

金融代写|金融微积分代写Finance Calculus代考|Discrete and Continuous Random Variables

伯努利分布。让 $X$ 是一个伯努利随机变量， $X \sim \operatorname{Bernoulli}(p), p \in[0,1]$ 具有概率质量函数
$$
\mathbb{P}(X=1)=p, \quad \mathbb{P}(X=0)=1-p .
$$
显示 $\mathrm{E}(X)=p$ 和 $\operatorname{Var}(X)=p(1-p)$.
解决方案: 如果 $X \sim \operatorname{Bernoulli}(p)$ 然后我们可以写
$$
\mathbb{P}(X=x)=p^{x}(1-p)^{1-x}, \quad x \in 0,1
$$
并且根据定义
$$
\mathbb{E}(X)=\sum_{x=0}^{1} x \mathbb{P}(X=x)=0 \cdot(1-p)+1 \cdot p=p \mathbb{E}\left(X^{2}\right)=\sum_{x=0}^{1} x^{2} \mathbb{P}(X=x)=0 \cdot(1-p)+1
$$
因此
$$
\mathbb{F}(X)=p, \quad \operatorname{Var}(X)=\mathbb{F}\left(X^{2}\right)-\mathbb{F}(X)^{2}=p(1-p)
$$
二项分布。让 $\$ left {X_{{i}\right } } _ { – } { i = 1 } \wedge { n } \text { 是一系列独立的伯努利随机变量，每个变量都具有概率质量函数 }
$\$ \$$
$\$ \$$
然后让
$\$ \$$
X=Isum_{i=1}^{n} X_{i}。
$\$ \$$
显示 $X$ 服从二项分布， $X \sim \operatorname{Binomial}(n, p)$ 具有概率质量函数
$\$ \$$
Imathbb ${P}(X=k)=\operatorname{left}(\mathrm{lbegin}{\operatorname{array}}{}$
$n \backslash$
k
lend{array $}$ \right } ) p ^ { \wedge } { k } ( 1 – p ) ^ { \wedge } { n k } \text { , Iquad } k = 0 , 1 , 2 , \backslash \text { dots, } n
$\$ \$$
这样 $\mathrm{E}(X)=n p$ 和 $\operatorname{Var}(X)=n p(1-p)$.
使用中心极限定理表明 $X$ 近似正态分布， $X \div \mathcal{N}(n p, n p(1-p))$ 作为 $n \rightarrow \infty$.

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考

随机过程代考

贝叶斯方法代考

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

机器学习代写

多元统计分析代考

基础数据: $N$ 个样本， $P$ 个变量数的单样本，组成的横列的数据表
变量定性: 分类和顺序；变量定量：数值
数学公式的角度分为: 因变量与自变量

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|CS5850

Posted on 2022年8月27日2022年8月27日 by statistics-lab

如果你也在怎样代写计算数学基础Mathematical Foundations of Computing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。

数学方面集中于计算机使用的领域，或与计算机科学相关的领域，即代数、一般拓扑学、数论、组合学和逻辑。计算方面的例子包括计算复杂性、并发性和量子计算。

statistics-lab™ 为您的留学生涯保驾护航在代写计算数学基础Mathematical Foundations of Computing方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写计算数学基础Mathematical Foundations of Computing方面经验极为丰富，各种代写计算数学基础Mathematical Foundations of Computing相关的作业也就用不着说。

我们提供的计算数学基础Mathematical Foundations of Computing及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等楖率论
Advanced Mathematical Statistics 高等数理统计学
(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

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|CS5850

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|Random Variables

So far, we have restricted ourselves to studying events, which are collections of outcomes of experiments or observations. However, we are often interested in abstract quantities or outcomes of experiments that are derived from events and observations but are not themselves events or observations. For example, if we throw a fair die, we may want to compute the probability that the square of the face value is smaller than 10. This is random and can be associated with a probability and, moreover, depends on some underlying random events. Yet, it is neither an event nor an observation: It is a random variable. Intuitively, a random variable is a quantity that can assume any one of a set of values, called its domain $\boldsymbol{D}$, and whose value can be stated only probabilistically. In this section, we will study random variables and their distributions.

More formally, a real random variable-the one most commonly encountered in applications having to do with computer networking-is a mapping from events in a sample space $S$ to the domain of real numbers. The probability associated with each value assumed by a real random variable ${ }^{2}$ is the probability of the underlying event in the sample space, as illustrated in Figure 1.1.

A random variable is discrete if the set of values it can assume is finite and countable. The elements of $D$ should be mutually exclusive-that is, the random variable cannot simultaneously take on more than one value-and exhaustive-the random variable cannot assume a value that is not an element of $D$.

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|Cumulative Density Function

The domain of a discrete real random variable $X_{d}$ is totally ordered; that is, for any two values $x_{1}$ and $x_{2}$ in the domain, either $x_{1}>x_{2}$ or $x_{2}>x_{1}$. We define the cumulative density function $F\left(X_{d}\right)$ by
$$
F(x)=\sum_{i \mid x_{i} \leq x} p\left(x_{i}\right)=p\left(X_{d} \leq x\right)
$$
Note the difference between $F\left(X_{d}\right)$, which denotes the cumulative distribution of random variable $X_{d}$, and $F(x)$, which is the value of the cumulative distribution for the value $X_{d}=x$

Similarly, the cumulative density function of a continuous random variable $X_{c}$, denoted $F\left(X_{c}\right)$, is given by
$$
F(x)=\int_{-\infty}^{x} f(y) d y=p\left(X_{c} \leq x\right)
$$
By definition of probability, in both cases, $0 \leq F\left(X_{d}\right) \leq 1,0 \leq F\left(X_{c}\right) \leq 1$.

计算数学基础代考

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|Random Variables

到目前为止，我们仅限于研究事件，即实验或观察结果的集合。然而，我们经常对源自事件和观察但本身不是事件或观察的实验的抽象数量或结果感兴趣。例如，如果我们掷出一个公平的骰子，我们可能想要计算面值平方小于 10 的概率。这是随机的，可以与概率相关联，此外，还取决于一些潜在的随机事件。然而，它既不是事件也不是观察：它是一个随机变量。直观地说，随机变量是一个可以假设一组值中的任何一个的量，称为它的域D，并且其值只能以概率方式表示。在本节中，我们将研究随机变量及其分布。

更正式地说，真正的随机变量（在与计算机网络有关的应用程序中最常见的变量）是样本空间中事件的映射小号到实数域。与实随机变量假设的每个值相关的概率2是样本空间中潜在事件的概率，如图 1.1 所示。

如果随机变量可以假设的一组值是有限且可数的，则它是离散的。的元素D应该是互斥的——也就是说，随机变量不能同时取多个值——并且是穷尽的——随机变量不能取一个不是D.

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|Cumulative Density Function

离散实随机变量的域 $X_{d}$ 完全有序; 也就是说，对于任何两个值 $x_{1}$ 和 $x_{2}$ 在域中，要么 $x_{1}>x_{2}$ 或者 $x_{2}>x_{1}$. 我们定义男积密度函数 $F\left(X_{d}\right)$ 经过
$$
F(x)=\sum_{i \mid x_{i} \leq x} p\left(x_{i}\right)=p\left(X_{d} \leq x\right)
$$
注意之间的区别 $F\left(X_{d}\right)$ ，表示随机变量的男积分布 $X_{d}$ ，和 $F(x)$ ，这是该值的男积分布的值 $X_{d}=x$
类似地，连续随机变量的傫积密度函数 $X_{c}$ ，表示 $F\left(X_{c}\right)$ ，是（准）给的
$$
F(x)=\int_{-\infty}^{x} f(y) d y=p\left(X_{c} \leq x\right)
$$
根据概率的定义，在这两种情况下， $0 \leq F\left(X_{d}\right) \leq 1,0 \leq F\left(X_{c}\right) \leq 1$.

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考

随机过程代考

贝叶斯方法代考

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

机器学习代写

多元统计分析代考

基础数据: $N$ 个样本， $P$ 个变量数的单样本，组成的横列的数据表
变量定性: 分类和顺序；变量定量：数值
数学公式的角度分为: 因变量与自变量

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|CSMAX170

Posted on 2022年8月27日2022年8月27日 by statistics-lab

如果你也在怎样代写计算数学基础Mathematical Foundations of Computing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。

我们提供的计算数学基础Mathematical Foundations of Computing及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等楖率论
Advanced Mathematical Statistics 高等数理统计学
(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

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|CSMAX170

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|Joint and Conditional Probability

Thus far, we have defined the terms used in studying probability and considered single events in isolation. Having set this foundation, we now turn our attention to the interesting issues that arise when studying sequences of events. In doing so, it is very important to keep track of the sample space in which the events are defined: A common mistake is to ignore the fact that two events in a sequence may be defined on different sample spaces.

Consider two processes with sample spaces $S_{1}$ and $S_{2}$ that occur one after the other. The two processes can be viewed as a single joint process whose outcomes are the tuples chosen from the product space $S_{1} \times S_{2}$. We refer to the subsets of the product space as joint events. Just as before, we can associate probabilities with outcomes and events in the product space. To keep things straight, in this section, we denote the sample space associated with a probability as a subscript, so that $P_{S_{1}}(E)$ denotes the probability of event $E$ defined over sample space $S_{1}$, and $P_{S_{1} \times S_{2}}(E)$ is an event defined over the product space $S_{1} \times S_{2}$.
EXAMPLE 1.10: JOINT PROCESS AND JOINT EVENTS
Consider sample space $S_{1}={1,2,3}$ and sample space $S_{2}={a, b, c}$. Then, the product space is given by ${(1, a),(1, b),(1, c),(2, a),(2, b),(2, c),(3, a),(3, b)$, $(3, c)}$. If these events are equiprobable, the probability of each tuple is $\frac{1}{9}$. Let $E={1,2}$ be an event in $S_{1}$ and $F={b}$ be an event in $S_{2}$. Then, the event $E F$ is given by the tuples ${(1, b),(2, b)}$ and has probability $\frac{1}{9}+\frac{1}{9}=\frac{2}{9}$.
We will return to the topic of joint processes in Section $1.8$. We now turn our attention to the concept of conditional probability.

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|Bayes’s Rule

One of the most widely used rules in the theory of probability is due to an English country minister: Thomas Bayes. Its significance is that it allows us to infer “backwards” from effects to causes rather than from causes to effects. The derivation of his rule is straightforward, though its implications are profound.
We begin with the definition of conditional probability (Equation 1.4):
$$
P_{S \times S}(F \mid E)=\frac{P_{S \times S}(E F)}{P_{S}(E)}
$$
If the underlying sample spaces can be assumed to be implicitly known, we can rewrite this as
$$
P(E F)=P(F \mid E) P(E)
$$
We interpret this to mean that the probability that both $E$ and $F$ occur is the product of the probabilities of two events: first, that $E$ occurs; second, that conditional on $E, F$ occurs.

Recall that $P(F \mid E)$ is defined in terms of the event $F$ following event $E$. Now, consider the converse: $F$ is known to have occurred. What is the probability that $E$ occurred? This is similar to the problem: If there is fire, there is smoke, but if we see smoke, what is the probability that it was due to a fire? The probability we want is $P(E \mid F)$. Using the definition of conditional probability, it is given by
$$
P(E \mid F)=\frac{P(E F)}{P(F)}
$$
Substituting for $P(F)$ from Equation 1.7, we get
$$
P(E \mid F)=\frac{P(F \mid E)}{P(F)} P(E)
$$
which is Bayes’s rule. One way of interpreting this is that it allows us to compute the degree to which some effect, or posterior $F$, can be attributed to some cause, or prior $E$.

计算数学基础代考

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|Joint and Conditional Probability

到目前为止，我们已经定义了用于研究概率的术语，并孤立地考虑了单个事件。在奠定了这个基础之后，我们现在将注意力转向研究事件序列时出现的有趣问题。这样做时，跟踪定义事件的样本空间非常重要: 一个常见的错误是忽略序列中的两个事件可能在不同的样本空间上定义的事实。
考虑两个具有样本空间的过程 $S_{1}$ 和 $S_{2}$ 一个接一个地发生。这两个过程可以看作是一个单一的联合过程，其结果是从产品空间中选择的元组 $S_{1} \times S_{2}$. 我们将产品空间的子集称为联合事件。和以前一样，我们可以将概率与产品空间中的结果和事件联系起来。为了保持直截了当，在本节中，我们将与概率相关的样本空间表示为下标，因此 $P_{S_{1}}(E)$ 表示事件的概率 $E$ 在样本空间上定义 $S_{1}$ ，和 $P_{S_{1} \times S_{2}}(E)$ 是在产品空间上定义的事件 $S_{1} \times S_{2}$.
例 1.10：联合过程和联合事件
考虑样本空间 $S_{1}=1,2,3$ 和样本空间 $S_{2}=a, b, c$. 然后，产品空间由下式给出
$(1, a),(1, b),(1, c),(2, a),(2, b),(2, c),(3, a),(3, b) \$, \$(3, c)$. 如果这些事件是等概率的，则每个元组的概率为 $\frac{1}{9}$. 让 $E=1,2$ 成为一个事件 $S_{1}$ 和 $F=b$ 成为一个事件 $S_{2}$. 那么，事件 $E F$ 由元组给出 $(1, b),(2, b)$ 并且有概率 $\frac{1}{9}+\frac{1}{9}=\frac{2}{9}$.
我们将在第 1 节回到联合过程的主题。1.8. 我们现在将注意力转向条件概率的概念。

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|Bayes’s Rule

概率论中使用最广泛的规则之一归功于一位英国国家部长：托马斯贝叶斯。它的意义在于它允许我们从结果到原因而不是从原因到结果来推断“倒退”。他的规则的推导是直截了当的，尽管其含义是深远的。我们从条件概率的定义开始 (公式 1.4）：
$$
P_{S \times S}(F \mid E)=\frac{P_{S \times S}(E F)}{P_{S}(E)}
$$
如果可以假设基础样本空间是隐式已知的，我们可以将其重写为
$$
P(E F)=P(F \mid E) P(E)
$$
我们将此解释为意味着两者的概率 $E$ 和 $F$ 发生是两个事件的概率的乘积：首先， $E$ 发生；第二，有条件的 $E, F$ 发生。
回顾 $P(F \mid E)$ 是根据事件定义的 $F$ 以下事件 $E$. 现在，考虑相反的情况: $F$ 已知发生。发生的概率是多少 $E$ 发生了? 这类似于问题: 如果有火，就有烟，但是如果我们看到烟，那么它是由火灾引起的概率是多少? 我们想要的概率是 $P(E \mid F)$. 使用条件概率的定义，它由下式给出
$$
P(E \mid F)=\frac{P(E F)}{P(F)}
$$
代替 $P(F)$ 从方程 1.7，我们得到
$$
P(E \mid F)=\frac{P(F \mid E)}{P(F)} P(E)
$$
这是贝叶斯规则。解释这一点的一种方法是，它允许我们计算某些影响或后验的程度 $F$ ，可以归因于某些原因，或先于 $E$.

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考

随机过程代考

贝叶斯方法代考

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

机器学习代写

多元统计分析代考

基础数据: $N$ 个样本， $P$ 个变量数的单样本，组成的横列的数据表
变量定性: 分类和顺序；变量定量：数值
数学公式的角度分为: 因变量与自变量

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|ECE3020

Posted on 2022年8月27日2022年8月27日 by statistics-lab

如果你也在怎样代写计算数学基础Mathematical Foundations of Computing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。

我们提供的计算数学基础Mathematical Foundations of Computing及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等楖率论
Advanced Mathematical Statistics 高等数理统计学
(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

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|ECE3020

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|Axioms of Probability

One of the breakthroughs in modern mathematics was the realization that the theory of probability can be derived from just a handful of intuitively obvious axioms. Several variants of the axioms of probability are known. We present the three axioms as stated by Kolmogorov to emphasize the simplicity and elegance that lie at the heart of probability theory.

$0 \leq P(E) \leq 1$; that is, the probability of an event lies between 0 and 1 .
$P(S)=1$, that is, it is certain that at least some event in $S$ will occur.
Given a potentially infinite set of mutually exclusive events $E_{1}, E_{2}, \ldots$
$$
P\left(\bigcup_{i=1}^{\infty} E_{i}\right)=\sum_{i=1}^{\infty} P\left(E_{i}\right)
$$
That is, the probability that any one of the events in the set of mutually exclusive events occurs is the sum of their individual probabilities. For any finite set of $n$ mutually exclusive events, we can state the axiom equivalently as
$$
P\left(\bigcup_{i=1}^{n} E_{i}\right)=\sum_{i=1}^{n} P\left(E_{i}\right)
$$
An alternative form of axiom 3 is:
$$
P\left(E_{1} \vee E_{2}\right)=P\left(E_{1}\right)+P\left(E_{2}\right)-P\left(E_{1} \wedge E_{2}\right)
$$
This alternative form applies to non-mutually exclusive events.

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|Subjective and Objective Probability

The axiomatic approach is indifferent as to how the probability of an event is determined. It turns out that there are two distinct ways in which to determine the probability of an event. In some cases, the probability of an event can be derived from counting arguments. For instance, given the roll of a fair die, we know that only six outcomes are possible and that all outcomes are equally likely, so that the probability of rolling, say, a 1 , is $1 / 6$. This is called its objective probability. Another way of computing objective probabilities is to define the probability of an event as being the limit of a counting process, as the next example shows.

Consider a measurement device that measures the packet header types of every packet that crosses a link. Suppose that during the course of a day, the device samples $1,000,000$ packets, of which 450,000 are UDP packets, 500,000 are TCP packets, and the rest are from other transport protocols. Given the large number of underlying observations, to a first approximation, we can consider the probability that a randomly selected packet uses the UDP protocol to be $450,000 / 1,000,000=0.45$. More precisely, we state
$$
P(U D P)=\operatorname{Lim}_{t \rightarrow \infty}(\operatorname{UDPCoun} t(t)) /(\text { TotalPacket } \operatorname{Coun}(t)),
$$
where UDPCount(t) is the number of UDP packets seen during a measurement interval of duration $t$, and TotalPacketCount $(t)$ is the total number of packets seen during the same measurement interval. Similarly, $P(T C P)=0.5$.

Note that in reality, the mathematical limit cannot be achieved, because no packet trace is infinite. Worse, over the course of a week or a month, the underlying workload could change, so that the limit may not even exist. Therefore, in practice, we are forced to choose “sufficiently large” packet counts and hope that the ratio thus computed corresponds to a probability. This approach is also called the frequentist approach to probability.

计算数学基础代考

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|Axioms of Probability

现代数学的突破之一是认识到概率论可以从少数直观的公理推导出来。概率公理的几种变体是已知的。我们提出 Kolmogorov 所说的三个公理，以强调概率论核心的简单性和优雅性。

$0 \leq P(E) \leq 1$; 也就是说，事件的概率介于 0 和 1 之间。
$P(S)=1$ ，也就是说，可以肯定的是，至少有一些事件发生在 $S$ 会发生。
给定一组可能无限的互斥事件 $E_{1}, E_{2}, \ldots$
$$
P\left(\bigcup_{i=1}^{\infty} E_{i}\right)=\sum_{i=1}^{\infty} P\left(E_{i}\right)
$$
也就是说，这组互斥事件中任何一个事件发生的概率是它们各自概率的总和。对于任何有限集 $n$ 互斥事件，我们可以将公理等价地表述为
$$
P\left(\bigcup_{i=1}^{n} E_{i}\right)=\sum_{i=1}^{n} P\left(E_{i}\right)
$$
公理 3 的另一种形式是:
$$
P\left(E_{1} \vee E_{2}\right)=P\left(E_{1}\right)+P\left(E_{2}\right)-P\left(E_{1} \wedge E_{2}\right)
$$
这种替代形式适用于非互斥事件。

电子工程代写|计算数学基础代写Mathematical Foundations of Computing代考|Subjective and Objective Probability

公理化方法与如何确定事件的概率无关。事实证明，有两种不同的方法可以确定事件的概率。在某些情况下，事件的概率可以通过计算参数得出。例如，给定一个公平骰子的郑骰，我们知道只有六个结果是可能的，并且所有结果的可能性都相同，因此郑骰子的概率，比如说， 1 ，是 $1 / 6$. 这称为它的客观概率。计算客观概率的另一种方法是将事件的概率定义为计数过程的极限，如下一个示例所示。
考虑一个测量设备，它测量通过链路的每个数据包的数据包头类型。假设在一天的过程中，设备采样 $1,000,000$ 数据包，其中 450,000 个是 UDP 数据包， 500,000 个是 TCP 数据包，其余来自其他传输协议。给定大量的底层观察，我们可以认为随机选择的数据包使用 UDP 协议的概率为 $450,000 / 1,000,000=0.45$. 更准确地说，我们声明
$$
P(U D P)=\operatorname{Lim}_{t \rightarrow \infty}(\operatorname{UDPCoun} t(t)) /(\text { TotalPacket Coun }(t)),
$$
其中 UDPCount $(\mathrm{t})$ 是在持续时间的测量间隔内看到的 UDP 数据包的数量 $t$, 和 TotalPacketCount $(t)$ 是在同一测量间隔内看到的数据包总数。相似地， $P(T C P)=0.5$.
请注意，实际上，无法达到数学极限，因为没有数据包跟踪是无限的。更糟糕的是，在一周或一个月的过程中，基础工作量可能会发生变化，因此限制甚至可能不存在。因此，在实践中，我们被迫选择“足够大”的数据包计数，并希望由此计算的比率对应于概率。这种方法也被称为概率的频率论方法。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考

随机过程代考

贝叶斯方法代考

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

机器学习代写

多元统计分析代考

基础数据: $N$ 个样本， $P$ 个变量数的单样本，组成的横列的数据表
变量定性: 分类和顺序；变量定量：数值
数学公式的角度分为: 因变量与自变量

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

电子工程代写|计算机及网络安全代写Computer and Network Security代考|CS155

Posted on 2022年8月27日2022年8月27日 by statistics-lab

如果你也在怎样代写计算机及网络安全Computer and Network Security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。

网络安全保护你的网络和数据免受破坏、入侵和其他威胁。这是一个庞大的总括性术语，描述了硬件和软件解决方案，以及与网络使用、可访问性和整体威胁保护有关的流程或规则和配置。

statistics-lab™ 为您的留学生涯保驾护航在代写计算机及网络安全Computer and Network Security方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写计算机及网络安全Computer and Network Security方面经验极为丰富，各种代写计算机及网络安全Computer and Network Security相关的作业也就用不着说。

我们提供的计算机及网络安全Computer and Network Security及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等楖率论
Advanced Mathematical Statistics 高等数理统计学
(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

电子工程代写|计算机及网络安全代写Computer and Network Security代考|CS155

电子工程代写|计算机及网络安全代写Computer and Network Security代考|Multiplexing of Transmission Signals

Quite often during the transmission of data over a network medium, the volume of transmitted data may far exceed the capacity of the medium. Whenever this happens, it may be possible to make multiple signal carriers share a transmission medium. This is referred to as multiplexing. There are two ways in which multiplexing can be achieved: time-division multiplexing (TMD) and frequency- division multiplexing (FDM).

In FDM, all data channels are first converted to analog form. Since a number of signals can be carried on a carrier, each analog signal is then modulated by a separate and different carrier frequency, and this makes their recovery possible during the demultiplexing process. The frequencies are then bundled on the carrier. At the receiving end, the demultiplexer can select the desired carrier signal and use it to extract the data signal for that channel in such a way that the bandwidths do not overlap. FDM has the advantage of supporting full-duplex communication.

TDM, on the other hand, works by dividing the channel into time slots that are allocated to the data streams before they are transmitted. At both ends of the transmission, if the sender and receiver agree on the time-slot assignments, then the receiver can easily recover and reconstruct the original data streams. Thus, multiple digital signals can be carried on one carrier by interleaving portions of each signal in time.

电子工程代写|计算机及网络安全代写Computer and Network Security代考|Wired Transmission Media

Wired transmission media are used in fixed networks physically connecting every network element. There are different types of physical media, the most common of which are copper wires, twisted pairs, coaxial cables, and optical fibers.

Copper Wires have been traditionally used in communication because of their low resistance to electrical currents that allows signals to travel even further. However, copper wires suffer interference from electromagnetic energy in the environment, and thus they must always be insulated.

Twisted Pair is a pair of wires consisting of one insulated copper wire wrapped around the other, forming frequent and numerous twists. Together, the twisted, insulated copper wires act as a full-duplex communication link. The twisting of the wires reduces the sensitivity of the cable to electromagnetic interference and also reduces the radiation of radio frequency noises that may interfere with nearby cables and electronic components. The capacity of the transmitting medium can be increased by bundling more than one pair of the twisted wires together in a protective coating. Because twisted pairs were far less expensive, easy to install, and had a high quality of voice data, they were widely used in telephone networks. However, because they are poor in upward scalability in transmission rate, distance, and bandwidth in LANs, twisted pair technology has been abandoned in favor of other technologies. Figure $1.8$ shows a twisted pair.

Coaxial Cables are dual-conductor cables with a shared inner conductor in the core of the cable protected by an insulation layer and the outer conductor surrounding the insulation. These cables are called coaxial because they share the inner conductor. The inner core conductor is usually made of solid copper wire but, at times, can also be made up of stranded wire. The outer conductor, which is made of braided wires, but sometimes made of metallic foil or both, commonly forms a protective tube around the inner conductor. This outer conductor is also further protected by annther outer enating called the sheath. Figure $1.9$ shows a enaxial eable. Coaxial cables are commonly used in television transmissions. Unlike twisted pairs, coaxial cables can be used over long distances. There are two types of coaxial cables: thinnet, a light and flexible cabling medium that is inexpensive and easy to install, and the thicknet, which is thicker and harder to break and can carry more signals for a longer distance than thinnet.

计算机及网络安全代考

电子工程代写|计算机及网络安全代写Computer and Network Security代考|Multiplexing of Transmission Signals

很多时候，在通过网络介质传输数据的过程中，传输的数据量可能远远超过介质的容量。每当这种情况发生时，就有可能使多个信号载波共享一个传输介质。这称为多路复用。有两种方式可以实现复用：时分复用 (TMD) 和频分复用 (FDM)。

在 FDM 中，所有数据通道首先转换为模拟形式。由于在一个载波上可以承载多个信号，每个模拟信号然后由一个单独的不同载波频率调制，这使得它们在解复用过程中的恢复成为可能。然后将频率捆绑在载波上。在接收端，解复用器可以选择所需的载波信号，并使用它以带宽不重叠的方式提取该信道的数据信号。FDM 的优点是支持全双工通信。

另一方面，TDM 通过将信道划分为时隙来工作，这些时隙在数据流传输之前分配给这些时隙。在传输的两端，如果发送方和接收方就时隙分配达成一致，那么接收方就可以轻松地恢复和重建原始数据流。因此，通过在时间上交织每个信号的部分，可以在一个载波上承载多个数字信号。

电子工程代写|计算机及网络安全代写Computer and Network Security代考|Wired Transmission Media

有线传输介质用于物理连接每个网络元素的固定网络。有不同类型的物理介质，其中最常见的是铜线、双绞线、同轴电缆和光纤。

铜线传统上用于通信，因为它们对电流的低电阻允许信号传播得更远。然而，铜线会受到环境中电磁能量的干扰，因此必须始终保持绝缘。

双绞线是一对电线，由一根绝缘铜线缠绕在另一根上，形成频繁和无数的扭曲。绞合的绝缘铜线一起充当全双工通信链路。电线的绞合降低了电缆对电磁干扰的敏感度，也减少了可能干扰附近电缆和电子元件的射频噪声的辐射。通过将一对以上的双绞线捆绑在一起形成保护涂层，可以增加传输介质的容量。由于双绞线价格低廉、易于安装且语音数据质量高，因此被广泛用于电话网络。但是，由于它们在 LAN 中的传输速率、距离和带宽的向上扩展性较差，双绞线技术已被弃用，取而代之的是其他技术。数字1.8显示双绞线。

同轴电缆是双芯电缆，在电缆的芯线中具有共享的内导体，由绝缘层保护，外导体围绕绝缘层。这些电缆被称为同轴电缆，因为它们共享内部导体。内芯导体通常由实心铜线制成，但有时也可以由绞合线制成。外导体由编织线制成，但有时由金属箔或两者制成，通常在内导体周围形成保护管。该外导体还受到称为护套的另一个外层的进一步保护。数字1.9显示了一个同轴eable。同轴电缆通常用于电视传输。与双绞线不同，同轴电缆可以长距离使用。同轴电缆有两种类型：薄网，一种轻便灵活的布线介质，价格低廉，易于安装；厚网，比薄网更厚，更难断裂，可以在更长的距离内传输更多信号。

电子工程代写|计算机及网络安全代写Computer and Network Security代考请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写