Learning Bayesian Models with R

Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems

About This Book

• Understand the principles of Bayesian Inference with less mathematical equations
• Learn state-of-the art Machine Learning methods
• Familiarize yourself with the recent advances in Deep Learning and Big Data frameworks with this step-by-step guide

Who This Book Is For

This book is for statisticians, analysts, and data scientists who want to build a Bayes-based system with R and implement it in their day-to-day models and projects. It is mainly intended for Data Scientists and Software Engineers who are involved in the development of Advanced Analytics applications. To understand this book, it would be useful if you have basic knowledge of probability theory and analytics and some familiarity with the programming language R.

What You Will Learn

• Set up the R environment
• Create a classification model to predict and explore discrete variables
• Get acquainted with Probability Theory to analyze random events
• Build Linear Regression models
• Use Bayesian networks to infer the probability distribution of decision variables in a problem
• Model a problem using Bayesian Linear Regression approach with the R package BLR
• Use Bayesian Logistic Regression model to classify numerical data
• Perform Bayesian Inference on massively large data sets using the MapReduce programs in R and Cloud computing

In Detail

Bayesian Inference provides a unified framework to deal with all sorts of uncertainties when learning patterns form data using machine learning models and use it for predicting future observations. However, learning and implementing Bayesian models is not easy for data science practitioners due to the level of mathematical treatment involved. Also, applying Bayesian methods to real-world problems requires high computational resources. With the recent advances in computation and several open sources packages available in R, Bayesian modeling has become more feasible to use for practical applications today. Therefore, it would be advantageous for all data scientists and engineers to understand Bayesian methods and apply them in their projects to achieve better results.

Learning Bayesian Models with R starts by giving you a comprehensive coverage of the Bayesian Machine Learning models and the R packages that implement them. It begins with an introduction to the fundamentals of probability theory and R programming for those who are new to the subject. Then the book covers some of the important machine learning methods, both supervised and unsupervised learning, implemented using Bayesian Inference and R.

Every chapter begins with a theoretical description of the method explained in a very simple manner. Then, relevant R packages are discussed and some illustrations using data sets from the UCI Machine Learning repository are given. Each chapter ends with some simple exercises for you to get hands-on experience of the concepts and R packages discussed in the chapter.

The last chapters are devoted to the latest development in the field, specifically Deep Learning, which uses a class of Neural Network models that are currently at the frontier of Artificial Intelligence. The book concludes with the application of Bayesian methods on Big Data using the Hadoop and Spark frameworks.

Style and approach

The book first gives you a theoretical description of the Bayesian models in simple language, followed by details of its implementation in the R package. Each chapter has illustrations for the use of Bayesian model and the corresponding R package, using data sets from the UCI Machine Learning repository. Each chapter also contains sufficient exercises for you to get more hands-on practice.

成為使用R的貝葉斯機器學習方法的專家，並應用它們來解決現實世界的大數據問題。

關於本書
- 以較少的數學方程式了解貝葉斯推論的原則
- 學習最先進的機器學習方法
- 透過逐步指南熟悉深度學習和大數據框架的最新進展

本書適合對統計學家、分析師和數據科學家，希望使用R構建基於貝葉斯的系統並在日常模型和項目中實施的讀者。主要針對參與高級分析應用程序開發的數據科學家和軟件工程師。為了理解本書，最好具備概率理論和分析的基本知識，以及對R編程語言的一些熟悉。

你將學到什麼
- 設置R環境
- 創建分類模型以預測和探索離散變量
- 熟悉概率理論以分析隨機事件
- 構建線性回歸模型
- 使用貝葉斯網絡推斷問題中決策變量的概率分布
- 使用R套件BLR以貝葉斯線性回歸方法建模問題
- 使用貝葉斯邏輯回歸模型對數值數據進行分類
- 使用R中的MapReduce程序和雲計算對大規模數據集進行貝葉斯推論

詳細內容
貝葉斯推論提供了一個統一的框架，用於在使用機器學習模型從數據中學習模式並用於預測未來觀察時處理各種不確定性。然而，由於涉及到的數學處理水平，學習和實施貝葉斯模型對於數據科學從業人員來說並不容易。此外，將貝葉斯方法應用於現實世界的問題需要高計算資源。隨著計算能力的最新進展和R中提供的幾個開源套件，貝葉斯建模在今天的實際應用中變得更加可行。因此，對於所有數據科學家和工程師來說，了解貝葉斯方法並在項目中應用它們以獲得更好的結果將是有利的。

《使用R學習貝葉斯模型》首先以簡單的語言為您提供貝葉斯模型的理論描述，然後介紹實現該模型的R套件的詳細信息。每章都有使用UCI機器學習存儲庫中的數據集的貝葉斯模型和相應R套件的示例。每章還包含足夠的練習，讓您更多地進行實踐。

最後幾章專門介紹了該領域的最新發展，特別是深度學習，它使用一類神經網絡模型，目前處於人工智能的前沿。本書以在Hadoop和Spark框架上應用貝葉斯方法於大數據的方式結束。

風格和方法
本書首先以簡單的語言給出貝葉斯模型的理論描述，然後介紹了R套件的實現細節。每章都有使用UCI機器學習存儲庫中的數據集的貝葉斯模型和相應R套件的示例。每章還包含足夠的練習，讓您更多地進行實踐。