统计代写 | Statistical Learning and Decision Making代考|Acknowledgments

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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|Acknowledgments

统计代写 | Statistical Learning and Decision Making代考|Decision Making

An agent is an entity that acts based on observations of its environment. Agents may be physical entities, like humans or robots, or they may be nonphysical entities, such as decision support systems that are implemented entirely in software. As shown in figure 1.1, the interaction between the agent and the environment follows an observe-act cycle or loop.

The agent at time $t$ receives an obseroation of the environment, denoted $o_{l}$. Observations may be made, for example, through a biological sensory process as in humans or by a sensor system like radar in an air traffic control system. Observations are often incomplete or noisy; humans may not see an approaching aircraft and a radar system might miss a detection through electromagnetic interference. The agent then chooses an action $a_{t}$ through some decision-making

process. This action, such as sounding an alert, may have a nondeterministic effect on the environment.

Our focus is on agents that interact intelligently to achieve their objectives over time. Given the past sequence of observations $o_{1}, \ldots, o_{l}$ and knowledge about the environment, the agent must choose an action $a_{\ell}$ that best achieves its objectives in the presence of various sources of uncertainty, ${ }^{1}$ including:

  1. outcome uncertainty, where the effects of our actions are uncertain,
  2. model uncertainty, where our model of the problem is uncertain,
  3. state uncertainty, where the true state of the environment is uncertain, and
  4. interaction uncertainty, where the behavior of the other agents interacting in the environment is uncertain.

This book is organized around these four sources of uncertainty. Making decisions in the presence of uncertainty is central to the field of artificial intelligence ${ }^{2}$ as well as many other fields, as outlined in section 1.4. We will discuss a variety of algorithms, or descriptions of computational processes, for making decisions that are robust to uncertainty.

统计代写 | Statistical Learning and Decision Making代考|Applications

The decision making framework presented in the previous section can be applied to a wide variety of domains. This section discusses a few conceptual examples with real-world applications. Appendix F outlines additional notional examples that are used throughout this text to demonstrate the algorithms we discuss.

To help prevent mid-air collisions between aircraft, we want to design a system that can alert pilots to potential threats and direct them how to maneuver. ${ }^{3}$ The system communicates with the transponders of other aircraft to identify their positions with some degree of accuracy. Deciding what guidance to provide to the pilots from this information is challenging. There is uncertainty in how quickly the pilots will respond and how strongly they will comply with the guidance. In addition, there is uncertainty in the behavior of other aircraft in the vicinity. We want our system to alert sufficiently early to provide enough time for the pilots to maneuver the aircraft to avoid collision, but we do not want our system to alert too early and result in many unnecessary maneuvers. Since this system is to be used continuously worldwide, we need the system to provide an exceptional level of saftety.

统计代写 | Statistical Learning and Decision Making代考|Automated Driving

We want to build an autonomous vehicle that can safely drive in urban environments. 4 The vehicle must rely on a suite of sensors to perceive its environment to make safe decisions. One type of sensor is lidar, which involves measuring laser reflections off of the environment to determine distances to obstacles. Another type of sensor is a camera, which, through computer vision algorithms, can detect pedestrians and other vehicles. Both of these types of sensors are imperfect and susceptible to noise and occlusions. For example, a parked truck may occlude a pedestrian that may be trying to cross at a crosswalk. Our system must predict the intentions and future paths of other vehicles, pedestrians, and other road users from their observable behavior in order to safely navigate to our destination.

Worldwide, breast cancer is the most common cancer in women. Detecting breast cancer early can help save lives, with mammography being the most effective screening tool available. However, mammography carries with it potential risks, including false positives, which can result in unnecessary and invasive diagnostic followup. Research over the years has resulted in various population-based screening schedules based on age in order to balance testing benefits and risks. Developing a system that can make recommendations based on personal risk characteristics and screening history has the potential to result in better health outcomes. 5 The success of such a system can be compared to population-wide screening schedules in terms of total expected quality-adjusted life years, the number of mammograms, false-positives, and risk of undetected invasive cancer.

统计代写 | Statistical Learning and Decision Making代考|Acknowledgments


统计代写 | Statistical Learning and Decision Making代考|Decision Making

代理是基于对其环境的观察而采取行动的实体。代理可能是物理实体,例如人类或机器人,也可能是非物理实体,例如完全在软件中实现的决策支持系统。如图 1.1 所示,代理与环境之间的交互遵循一个观察-行为循环或循环。

当时的代理吨接收环境观测值,表示为这l. 例如,可以通过人类的生物感觉过程或空中交通管制系统中的雷达等传感器系统进行观察。观察往往不完整或嘈杂;人类可能看不到接近的飞机,雷达系统可能会因电磁干扰而错过检测。然后代理选择一个动作一种吨通过一些决策



  1. 结果不确定性,我们行动的影响是不确定的,
  2. 模型不确定性,我们的问题模型不确定,
  3. 状态不确定性,即环境的真实状态是不确定的,以及
  4. 交互不确定性,其中在环境中交互的其他代理的行为是不确定的。

本书围绕这四种不确定性来源进行组织。在存在不确定性的情况下做出决策是人工智能领域的核心2以及许多其他领域,如第 1.4 节所述。我们将讨论各种算法或计算过程的描述,以做出对不确定性具有鲁棒性的决策。

统计代写 | Statistical Learning and Decision Making代考|Applications

上一节中介绍的决策框架可以应用于各种领域。本节讨论一些具有实际应用程序的概念示例。附录 F 概述了在本文中用于演示我们讨论的算法的其他概念性示例。


统计代写 | Statistical Learning and Decision Making代考|Automated Driving

我们希望打造一款可以在城市环境中安全驾驶的自动驾驶汽车。4 车辆必须依靠一套传感器来感知其环境以做出安全决策。一种传感器是激光雷达,它涉及测量环境中的激光反射以确定到障碍物的距离。另一种传感器是摄像头,通过计算机视觉算法,可以检测行人和其他车辆。这两种类型的传感器都不完善,容易受到噪声和遮挡的影响。例如,停放的卡车可能会挡住可能试图在人行横道上过马路的行人。我们的系统必须根据可观察到的行为预测其他车辆、行人和其他道路使用者的意图和未来路径,以便安全地导航到我们的目的地。

在世界范围内,乳腺癌是女性最常见的癌症。及早发现乳腺癌有助于挽救生命,而乳房 X 光检查是最有效的筛查工具。然而,乳房 X 光检查带有潜在风险,包括误报,这可能导致不必要的侵入性诊断随访。多年来的研究导致了基于年龄的各种基于人群的筛查计划,以平衡测试的益处和风险。开发一个可以根据个人风险特征和筛查历史提出建议的系统有可能带来更好的健康结果。5 这种系统的成功可以与全人群筛查计划相比较,包括总预期质量调整生命年、乳房 X 线照片的数量、假阳性、

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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。





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。


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 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。



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