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数据和预测模型是决策中一个越来越重要的部分。
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我们提供的Statistical Learning and Decision Making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
统计代写 | Statistical Learning and Decision Making代考|Societal Impact
Algorithmic approaches to decision making have transformed society and will likely continue to in the future. This section briefly highlights a few ways decision making algorithms can contribute to society and introduces challenges that remain in ensuring broad benefit. ${ }^{26}$
Algorithmic approaches have contributed to environmental sustainability. In the context of energy management, for example, Bayesian optimization has been applied to automated home energy management systems. Algorithms from the field of multi-agent systems are used to predict the operation of smart grids, design markets for trading energy, and predict rooftop solar-power adoption. Algorithms have also been developed to protect biodiversity. For example, neural networks are used to automate wildlife censuses, game-theoretic approaches are used to combat poaching in forests, and optimization techniques are employed to allocate resources for habitat management.
Decision making algorithms have found success in the field of medicine for decades. Such algorithms have been used for matching residents to hospitals and matching organ donors to patients in need. An early application of Bayesian
networks, which we will cover in the first part of this book, was disease diagnosis. Since then, Bayesian networks have been widely used in medicine for diagnosis and prognosis of many diseases such as cervical cancer, breast cancer, and glaucoma. The field of medical image processing has been transformed by deep learning, and recently, algorithmic ideas have played an important role in understanding the spread of disease.
Algorithms have enabled us to understand the growth of urban areas and facilitate their design. Data-driven algorithms have been widely used to improve public infrastructure. For example, stochastic processes have been used to predict failures in water pipelines, deep learning has improved the management of traffic, and Markov decision processes and Monte Carlo methods have been employed to improve emergency response. Ideas from decentralized multi-agent systems have optimized travel routes, and path planning techniques have been used to optimize delivery of goods. A major application of decision making algorithms in transportation has been in the development of autonomous cars and improving the safety of aircraft.
Algorithms for optimizing decisions can amplify the impact of its users, regardless of the nature of their intention. If the objective of the user of these algorithms, for example, is to spread misinformation during a political election, then optimization processes can help facilitate this. However, similar algorithms can be used to monitor and counteract the spread of false information. Sometimes the implementation of these decision making algorithms can lead to downstream consequences that were not intended by their users. 27
Although algorithms have the potential to bring significant benefits, there are also challenges associated with their implementation in society. Data-driven algorithms often suffer from inherent biases and blind spots due to the way data is collected. As algorithms become part of our lives, it is important to understand how the risk of bias can be reduced and how the benefits of algorithmic progress can be distributed in a manner that is equitable and fair. Algorithms can also be vulnerable to adversarial manipulation, and it is critical that we design algorithms that are robust to such attacks. It is also important to extend moral and legal frameworks for preventing unintended consequences and assigning responsibility.
统计代写 | Statistical Learning and Decision Making代考|Probabilistic Reasoning
Rational decision making requires reasoning about our uncertainty and objectives. This part of the book begins by discussing how to represent uncertainty as a probability distribution. Real-world problems require reasoning about distributions over many variables. We will discuss how to construct these models, how to use them to make inferences, and how to learn their parameters and structure from data. We then introduce the foundations of utility theory and show how it forms the basis for rational decision making under uncertainty through the maximum expected utility principle. We then discuss how notions of utility theory can be incorporated into the probabilistic graphical models introduced earlier to form what are called decision networks.
Many important problems require that we make a series of decisions. The same principle of maximum expected utility still applies, but optimal decision making in a sequential context requires reasoning about future sequences of actions and observations. This part of the book will discuss sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. We will focus on a general formulation of sequential decision problems under the assumption that the model is known and that the environment is fully observable. We will relax both of these assumptions later. Our discussion will begin with the introduction of the Markoo decision process (MDP), the standard mathematical model for sequential decision problems. We will discuss several approaches for finding exact solutions to these types of problems. Because large problems sometimes do not permit exact solutions to be efficiently found, we will discuss a collection of both offline and online approximate solution methods along with a type of method that involves directly searching the space of parameterized decision policies. Finally, we will discuss approaches for validating that our decislon strategies will perform as expected when deployed in the real world.
统计代写 | Statistical Learning and Decision Making代考|Model Uncertainty
In our discussion of sequential decision problems, we have assumed that the transition and reward models are known. In many problems, however, the dynamics and rewards are not known exactly, and the agent must learn to act through experience. By observing the outcomes of its actions in the form of state transitions and rewards, the agent is to choose actions that maximize its long-term accumulation of rewards. Solving such problems in which there is model uncertainty is the subject of the field of reinforcement learning and the focus of this part of the book. We will discuss several challenges in addressing model uncertainty. First, the agent must carefully balance exploration of the environment with the exploitation of knowledge gained through experience. Second, rewards may be received long after the important decisions have been made, so credit for later rewards must be assigned to earlier decisions. Third, the agent must generalize from limited experience. We will review the theory and some of the key algorithms for addressing these challenges.
统计代写
统计代写 | Statistical Learning and Decision Making代考|Societal Impact
决策制定的算法方法已经改变了社会，并且很可能在未来继续如此。本节简要强调决策算法可以为社会做出贡献的几种方式，并介绍在确保广泛利益方面仍然存在的挑战。26
算法方法有助于环境可持续性。例如，在能源管理方面，贝叶斯优化已应用于自动化家庭能源管理系统。多智能体系统领域的算法用于预测智能电网的运行、设计能源交易市场以及预测屋顶太阳能的采用。还开发了算法来保护生物多样性。例如，神经网络用于自动化野生动物普查，博弈论方法用于打击森林偷猎，优化技术用于分配资源用于栖息地管理。
几十年来，决策算法在医学领域取得了成功。这种算法已被用于将居民与医院进行匹配，并将器官捐赠者与有需要的患者进行匹配。贝叶斯的早期应用
我们将在本书第一部分介绍的网络是疾病诊断。此后，贝叶斯网络在医学上被广泛用于宫颈癌、乳腺癌、青光眼等多种疾病的诊断和预后。深度学习已经改变了医学图像处理领域，最近，算法思想在理解疾病的传播方面发挥了重要作用。
算法使我们能够了解城市地区的发展并促进其设计。数据驱动算法已被广泛用于改善公共基础设施。例如，随机过程已用于预测输水管道的故障，深度学习已改善交通管理，马尔可夫决策过程和蒙特卡罗方法已用于改善应急响应。分散式多智能体系统的想法优化了旅行路线，路径规划技术已被用于优化货物交付。决策算法在交通运输中的一个主要应用是开发自动驾驶汽车和提高飞机的安全性。
优化决策的算法可以放大其用户的影响，无论他们的意图是什么。例如，如果这些算法的用户的目标是在政治选举期间传播错误信息，那么优化过程可以帮助实现这一点。然而，类似的算法可用于监控和抵制虚假信息的传播。有时，这些决策算法的实施可能会导致其用户不希望出现的下游后果。27
尽管算法有可能带来显着的好处，但也存在与它们在社会中实施相关的挑战。由于收集数据的方式，数据驱动的算法经常存在固有的偏见和盲点。随着算法成为我们生活的一部分，了解如何降低偏见风险以及如何以公平公正的方式分配算法进步的好处非常重要。算法也可能容易受到对抗性操纵，因此我们设计对此类攻击具有鲁棒性的算法至关重要。扩展道德和法律框架以防止意外后果和分配责任也很重要。
统计代写 | Statistical Learning and Decision Making代考|Probabilistic Reasoning
理性的决策需要对我们的不确定性和目标进行推理。本书的这一部分首先讨论如何将不确定性表示为概率分布。现实世界的问题需要对许多变量的分布进行推理。我们将讨论如何构建这些模型，如何使用它们进行推断，以及如何从数据中学习它们的参数和结构。然后，我们介绍了效用理论的基础，并通过最大期望效用原则展示了它如何构成在不确定性下进行理性决策的基础。然后，我们讨论如何将效用理论的概念结合到前面介绍的概率图形模型中，以形成所谓的决策网络。
许多重要的问题需要我们做出一系列的决定。最大预期效用的相同原则仍然适用，但在顺序上下文中做出最佳决策需要对未来的行动和观察序列进行推理。本书的这一部分将讨论在我们的行动结果不确定的随机环境中的顺序决策问题。在模型已知且环境完全可观察的假设下，我们将专注于序列决策问题的一般表述。稍后我们将放宽这两个假设。我们的讨论将从介绍 Markoo 决策过程 (MDP) 开始，MDP 是顺序决策问题的标准数学模型。我们将讨论几种方法来找到这些类型问题的精确解决方案。因为大问题有时不允许有效地找到精确的解决方案，我们将讨论离线和在线近似解决方法的集合，以及一种涉及直接搜索参数化决策策略空间的方法。最后，我们将讨论验证我们的决策策略在现实世界中部署时是否按预期执行的方法。
统计代写 | Statistical Learning and Decision Making代考|Model Uncertainty
在我们对顺序决策问题的讨论中，我们假设转移和奖励模型是已知的。然而，在许多问题中，动态和奖励并不准确，智能体必须学会通过经验采取行动。通过以状态转换和奖励的形式观察其行为的结果，代理将选择最大化其长期奖励积累的行为。解决此类存在模型不确定性的问题是强化学习领域的主题，也是本书这一部分的重点。我们将讨论解决模型不确定性的几个挑战。首先，智能体必须谨慎地平衡对环境的探索与对通过经验获得的知识的利用。其次，奖励可能会在做出重要决定后很久才收到，因此，后期奖励的功劳必须分配给较早的决策。第三，代理必须根据有限的经验进行概括。我们将回顾解决这些挑战的理论和一些关键算法。
统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。
金融工程代写
金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题，以及设计新的和创新的金融产品。
非参数统计代写
非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。
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。
术语 广义线性模型（GLM）通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归，以及方差分析和方差分析（仅含固定效应）。
有限元方法代写
有限元方法（FEM）是一种流行的方法，用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。
有限元是一种通用的数值方法，用于解决两个或三个空间变量的偏微分方程（即一些边界值问题）。为了解决一个问题，有限元将一个大系统细分为更小、更简单的部分，称为有限元。这是通过在空间维度上的特定空间离散化来实现的，它是通过构建对象的网格来实现的：用于求解的数值域，它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统，以模拟整个问题。然后，有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。
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随机分析代写
随机微积分是数学的一个分支，对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。
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。