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# 计算机代写|图形模型代考Graphical Models代写|CS228 A Brief History

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## 计算机代写|图形模型代考Graphical Models代写|A Brief History

From an artificial intelligence perspective, we can consider the following stages in the development of uncertainty management techniques:

Beginnings (1950s and 60s)—artificial intelligence (AI) researchers focused on solving problems such as theorem proving, games like chess, and the “blocks world” planning domain, which do not involve uncertainty, making it unnecessary to develop techniques for managing uncertainty. The symbolic paradigm dominated $\mathrm{AI}$ in the beginnings.

Ad hoc techniques (1970s) – the development of expert systems for realistic applications such as medicine and mining, required the development of uncertainty management approaches. Novel ad hoc techniques were developed for specific expert systems, such as MYCIN’s certainty factors [16] and Prospector’s pseudo-probabilities [4]. It was later shown that these techniques had a set of implicit assumptions which limited their applicability [6]. Also in this period, alternative theories were pro- posed to manage uncertainty in expert systems, including fuzzy logic [18] and the Dempster-Shafer theory [15].

Resurgence of probability (1980s)—probability theory was used in some initial expert systems, however it was later discarded because its application in naive ways implies a high computational complexity (see Sect. 1.3). New developments, in particular Bayesian networks [10], make it possible to build complex probabilistic systems in an efficient manner, starting a new era for uncertainty management in AI.
Diverse formalisms (1990s)_Bayesian networks continued and were consolidated with the development of efficient inference and learning algorithms. Meanwhile, other techniques such as fuzzy and non-monotonic logics were considered as alternatives for reasoning under uncertainty.

Probabilistic graphical models (2000s) —several techniques based on probability and graphical representations were consolidated as powerful methods for representing, reasoning and making decisions under uncertainty, including Bayesian networks, Markov networks, influence diagrams and Markov decision processes, among others.

## 计算机代写|图形模型代考Graphical Models代写|Basic Probabilistic Models

Probability theory provides a well established foundation for managing uncertainty, therefore it is natural to use it for reasoning under uncertainty. However, if we apply probability in a naive way to complex problems, we are soon deterred by computational complexity

In this section we will show how we can model a problem using a naive probabilistic approach based on a flat representation; and then how we can use this representation to answer some probabilistic queries. This will help to understand the limitations of the basic approach, motivating the development of probabilistic graphical models. ${ }^{1}$

Many problems can be formulated as a set of variables, $X_{1}, X_{2}, \ldots X_{n}$ such that we know the values for some of these variables and the others are unknown. For instance, in medical diagnosis, the variables might represent certain symptoms and the associated diseases; usually we know the symptoms and we want to find the most probable disease(s). Another example could be a financial institution developing a system to help decide the amount of credit given to a certain customer. In this case the relevant variables are the attributes of the customer, i.e. age, income, previous credits, etc.; and a variable that represents the amount of credit to be given. Based on the customer attributes we want to determine, for instance, the maximum amount of credit that is safe to give to the customer. In general there are several types of problems that can be modeled in this way, such as diagnosis, classification, and perception problems; among others.

# 图形模型代写

## 计算机代写|图形模型代考Graphical Models代写|A Brief History

Ad hoc 技术（1970 年代）——为医学和采矿等实际应用开发专家系统，需要开发不确定性管理方法。为特定的专家系统开发了新的 ad hoc 技术，例如 MYCIN 的确定性因素 [16] 和 Prospector 的伪概率 [4]。后来表明，这些技术有一组隐含的假设，限制了它们的适用性[6]。同样在这一时期，提出了替代理论来管理专家系统中的不确定性，包括模糊逻辑 [18] 和 Dempster-Shafer 理论 [15]。

## 计算机代写|图形模型代考Graphical Models代写|Basic Probabilistic Models

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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。