使用R语言学习概率图模型

需积分: 9 150 浏览量更新于2024-07-20 收藏 10.85MB PDF 举报

"Learning Probabilistic Graphical Models in R 是一本由 David Bellot 编著，PACKT Publishing 在2016年出版的书籍。本书旨在通过实际问题和R语言的代码示例介绍概率图模型（Probabilistic Graphical Models，PGM）。PGM结合了概率论与图论，主要分为两种图形表示形式：贝叶斯网络和马尔科夫网络。R语言拥有许多用于实现图形模型的包。书中的内容首先引导读者将传统的统计模型转化为概率图模型，并探讨如何在图形模型中进行精确推理。接下来，作者会介绍一系列现代R包，帮助读者对模型进行推断，包括执行贝叶斯线性回归，展示在预测问题中采用概率方法的优势。随后，读者将深入学习如何使用R包并实践其技术。最后，书中会涉及机器学习应用，尤其在大数据领域的聚类和隐藏信息发现，以及PCA（主成分分析）和ICA（独立成分分析）这两种用于处理大规模问题的重要方法。本书适用于希望通过解决实际问题和R代码示例来学习概率图模型的读者，旨在提升他们在数据分析和机器学习领域的技能。" 本书的核心知识点包括： 1. **概率图模型基础**：理解PGM的基本概念，包括贝叶斯网络和马尔科夫网络的构建和性质。 2. **统计模型到PGM的转换**：学习如何将传统的统计模型如线性回归、逻辑回归等转换为图形模型。 3. **图形模型中的精确推理**：掌握在图形结构中进行概率推理的技术和算法。 4. **R包的应用**：了解并应用R中的`bnlearn`、`gRain`、`pgm`等包进行图形模型的建模和推断。 5. **贝叶斯线性回归**：通过实例演示贝叶斯方法在回归分析中的优势，尤其是在不确定性估计和预测中的应用。 6. **概率预测**：学习如何利用PGM进行更有效的预测，并对比传统统计方法。 7. **机器学习应用**：探讨PGM在聚类分析、大数据隐藏信息发现以及降维方法PCA和ICA中的应用。 8. **数据处理和降维**：PCA和ICA的理论与实践，用于处理大规模数据集，减少计算复杂度，提取关键信息。通过本书的学习，读者不仅能深入理解概率图模型的原理，还能掌握R语言在该领域的实践技巧，从而在实际数据分析和机器学习项目中更有效地解决问题。

Preface

[ ix ]

Piracy

Piracy of copyrighted material on the Internet is an ongoing problem across all

media. At Packt, we take the protection of our copyright and licenses very seriously.

If you come across any illegal copies of our works in any form on the Internet, please

provide us with the location address or website name immediately so that we can

pursue a remedy.

Please contact us at copyright@packtpub.com with a link to the suspected pirated

material.

We appreciate your help in protecting our authors and our ability to bring you

valuable content.

Questions

If you have a problem with any aspect of this book, you can contact us at

questions@packtpub.com, and we will do our best to address the problem.

www.it-ebooks.info

Probabilistic Reasoning

[ 2 ]

Therefore in this book, you will learn one of the most advanced techniques to

transform data into knowledge: machine learning. This technology is used in every

aspect of modern life now, from search engines, to stock market predictions, from

speech recognition to autonomous vehicles. Moreover it is used in many elds

where one would not suspect it at all, from quality assurance in product chains to

optimizing the placement of antennas for mobile phone networks.

Machine learning is the marriage between computer science and probabilities and

statistics. A central theme in machine learning is the problem of inference or how to

produce knowledge or predictions using an algorithm fed with data and examples.

And this brings us to the two fundamental aspects of machine learning: the design of

algorithms that can extract patterns and high-level knowledge from vast amounts of

data and also the design of algorithms that can use this knowledge—or, in scientic

terms: learning and inference.

Pierre-Simon Laplace (1749-1827) a French mathematician and one of the greatest

scientists of all time, was presumably among the rst to understand an important

aspect of data collection: data is unreliable, uncertain and, as we say today, noisy.

He was also the rst to develop the use of probabilities to deal with such aspects of

uncertainty and to represent one's degree of belief about an event or information.

In his Essai philosophique sur les probabilités (1814), Laplace formulated an original

mathematical system for reasoning about new and old data, in which one's belief

about something could be updated and improved as soon as new data where

available. Today we call that Bayesian reasoning. Indeed Thomas Bayes was

the rst, toward the end of the 18

century, to discover this principle. Without

any knowledge about Bayes' work, Pierre-Simon Laplace rediscovered the same

principle and formulated the modern form of the Bayes theorem. It is interesting

to note that Laplace eventually learned about Bayes' posthumous publications

and acknowledged Bayes to be the rst to describe the principle of this inductive

reasoning system. Today, we speak about Laplacian reasoning instead of Bayesian

reasoning and we call it the Bayes-Price-Laplace theorem.

More than a century later, this mathematical technique was reborn thanks to new

discoveries in computing probabilities and gave birth to one of the most important

and used techniques in machine learning: the probabilistic graphical model.

From now on, it is important to note that the term graphical refers to the theory of

graphs—that is, a mathematical object with nodes and edges (and not graphics or

drawings). You know that, when you want to explain to someone the relationships

between different objects or entities, you take a sheet of paper and draw boxes that

you connect with lines or arrows. It is an easy and neat way to show relationships,

whatever they are, between different elements.

www.it-ebooks.info

Chapter 1

[ 3 ]

Probabilistic Graphical Models (PGM for short) are exactly that: you want to

describe relationships between variables. However, you don't have any certainty

about your variables, but rather beliefs or uncertain knowledge. And we know now

that probabilities are the way to represent and deal with such uncertainties, in a

mathematical and rigorous way.

A probabilistic graphical model is a tool to represent beliefs and uncertain knowledge

about facts and events using probabilities. It is also one of the most advanced machine

learning techniques nowadays and has many industrial success stories.

Probabilistic graphical models can deal with our imperfect knowledge about the

world because our knowledge is always limited. We can't observe everything, we

can't represent all the universe in a computer. We are intrinsically limited as human

beings, as are our computers. With probabilistic graphical models, we can build

simple learning algorithms or complex expert systems. With new data, we can

improve those models and rene them as much as we can and also we can infer new

information or make predictions about unseen situations and events.

In this rst chapter you will learn about the fundamentals needed to understand

probabilistic graphical models; that is, probabilities and the simple rules of calculus on

which they are based. We will have an overview of what we can do with probabilistic

graphical models and the related R packages. These techniques are so successful that

we will have to restrict ourselves to just the most important R packages.

We will see how to develop simple models, piece by piece, like a brick game and

how to connect models together to develop even more advanced expert systems.

We will cover the following concepts and applications and each section will contain

numerical examples that you can directly use with R:

• Machine learning

• Representing uncertainty with probabilities

• Notions of probabilistic expert systems

• Representing knowledge with graphs

• Probabilistic graphical models

• Examples and applications

www.it-ebooks.info

剩余249页未读，继续阅读

vanridin

粉丝: 108
资源: 1187

使用R语言学习概率图模型

Learning Probabilistic Graphical Models in R

Learning-Probabilistic-Graphical-Models-in-R:学习R中的概率图形模型

Learning Probabilistic Graphical Models in R 原版pdf by Bellot

Mastering Probabilistic Graphical Models using Python(PACKT,2015)

Probabilistic Graphical Models

Visual learning and applications with the Probabilistic Graphical Models

Probabilistic Graphical Models slides

Probabilistic Graphical Models Part1

Probabilistic Graphical Models part2

Introduction to Probabilistic Graphical Models

最新资源