### 统计代写 | Statistical Learning and Decision Making代考| Societal Impact

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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 | 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.

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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。