### 统计代写 | Statistical Learning and Decision Making代考|Computer Science

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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|Computer Science

In the mid-twentieth century, computer scientists began formulating the problem of intelligent decision making as a problem of symbolic manipulation through formal logic. The computer program Logic Theorist, written in the mid-twentieth century to perform automated reasoning, used this way of thinking to prove mathematical theorems. Herbert Simon, one of its inventors, addressed the symbolic nature of the program by relating it to the human mind:
We invented a computer program capable of thinking non-numerically, and thereby solved the venerable mind/body problem, explaining how a system composed of matter can have the properties of mind. ${ }^{18}$
These symbolic systems relied heavily on human expertise. An alternative approach to intelligence, called connectionism, was inspired in part by developments in neuroscience and focuses on the use of artificial neural networks as a substrate for intelligence. With the knowledge that neural networks could be trained for pattern recognition, connectionists attempt to learn intelligent behavior from data or experience rather than the hard-coded knowledge of experts. The connectionist paradigm underpinned the success of AlphaGo, the autonomous program that beat a human professional at the game of Go, as well as much of the development of autonomous vehicles. Algorithms that combine both symbolic and connectionist paradigms remain an active area of research today.

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

The field of engineering has focused on allowing physical systems, such as robots, to make intelligent decisions. World-renowned roboticist Sebastian Thrun describes the components of these systems as follows:
Robotics systems have in common that they are situated in the physical world, perceive their environments through sensors, and manipulate their environment through things that move. ${ }^{19}$
To design these systems, engineers must address perception, planning, and acting. Physical systems perceive the world by using their sensors to create a representation of the salient features of their environment. The field of state-estimation has focused on using sensor measurements to construct a belief about the state of the world. Planning requires reasoning about the ways to execute the tasks they are designed to perform. The planning process has been enabled by advances in the semiconductor industry spanning many decades. ${ }^{20}$ Once a plan has been devised, an autonomous agent must act on it in the real world. This task requires both hardware in the form of actuators and algorithms to control the actuators and reject disturbances. The field of control theory has focused on the stabilization of mechanical systems through feedback control. ${ }^{21}$ Automatic control systems are widely used in industry, from the regulation of temperature in an oven to the navigation of aerospace systems.

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

An agent must be able to quantify its uncertainty in order to make informed decisions in uncertain environments. The field of decision making relies heavily on probability theory for this task. In particular, Bayesian statistics plays an important role in this text. In 1763 , a paper of Thomas Bayes was published posthumously containing what would later be known as Bayes’ rule. His approach to probabilistic inference fell in and out of favor until the mid-twentieth century, when researchers began to find Bayesian methods useful in a number of settings. ${ }^{22}$ Mathematician Bernard Koopman found practical use for the theory during World War II.
Every operation involved in search is beset with uncertainties; it can be understood quantitatively only in terms of [… probability. This may now be regarded as a truism, but it seems to have taken the developments in operational research of the Second World War to drive home its practical implications. ${ }^{23}$
Sampling-based methods (sometimes referred to as Monte Carlo methods) developed in the early twentieth century for large scale calculations as part of the Manhattan Project, made some inference techniques possible that would previously have been intractable. These foundations serve as a basis for Bayesian networks, which increased in popularity later in the twentieth century in the field of artificial intelligence.

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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。