Learning Bayesian Models with R

Packt Publishing Limited
  • 1. Auflage
  • |
  • erschienen am 28. Oktober 2015
  • |
  • 168 Seiten
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-1-78398-761-0 (ISBN)
Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problemsAbout This BookUnderstand the principles of Bayesian Inference with less mathematical equationsLearn state-of-the art Machine Learning methodsFamiliarize yourself with the recent advances in Deep Learning and Big Data frameworks with this step-by-step guideWho This Book Is ForThis 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 LearnSet up the R environmentCreate a classification model to predict and explore discrete variablesGet acquainted with Probability Theory to analyze random eventsBuild Linear Regression modelsUse Bayesian networks to infer the probability distribution of decision variables in a problemModel a problem using Bayesian Linear Regression approach with the R package BLRUse Bayesian Logistic Regression model to classify numerical dataPerform Bayesian Inference on massively large data sets using the MapReduce programs in R and Cloud computingIn DetailBayesian 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 approachThe 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.
  • Englisch
  • Birmingham
  • |
  • Großbritannien
978-1-78398-761-0 (9781783987610)
1783987618 (1783987618)
weitere Ausgaben werden ermittelt
Dr. Hari M. Koduvely is an experienced data scientist working at the Samsung R&D Institute in Bangalore, India. He has a PhD in statistical physics from the Tata Institute of Fundamental Research, Mumbai, India, and post-doctoral experience from the Weizmann Institute, Israel, and Georgia Tech, USA. Prior to joining Samsung, the author has worked for Amazon and Infosys Technologies, developing machine learning-based applications for their products and platforms. He also has several publications on Bayesian inference and its applications in areas such as recommendation systems and predictive health monitoring. His current interest is in developing large-scale machine learning methods, particularly for natural language understanding.
  • Cover
  • Copyright
  • Credits
  • About the Author
  • About the Reviewers
  • www.PacktPub.com
  • Table of Contents
  • Preface
  • Chapter 1: Introducing the Probability Theory
  • Probability distributions
  • Conditional probability
  • Bayesian theorem
  • Marginal distribution
  • Expectations and covariance
  • Binomial distribution
  • Beta distribution
  • Gamma distribution
  • Dirichlet distribution
  • Wishart distribution
  • Exercises
  • References
  • Summary
  • Chapter 2: The R Environment
  • Setting up the R environment and packages
  • Installing R and RStudio
  • Your first R program
  • Managing data in R
  • Data Types in R
  • Data structures in R
  • Importing data into R
  • Slicing and dicing datasets
  • Vectorized operations
  • Writing R programs
  • Control structures
  • Functions
  • Scoping rules
  • Loop functions
  • lapply
  • sapply
  • mapply
  • apply
  • tapply
  • Data visualization
  • High-level plotting functions
  • Low-level plotting commands
  • Interactive graphics functions
  • Sampling
  • Random uniform sampling from an interval
  • Sampling from normal distribution
  • Exercises
  • References
  • Summary
  • Chapter 3: Introducing Bayesian Inference
  • Bayesian view of uncertainty
  • Choosing the right prior distribution
  • Non-informative priors
  • Subjective priors
  • Conjugate priors
  • Hierarchical priors
  • Estimation of posterior distribution
  • Maximum a posteriori estimation
  • Laplace approximation
  • Monte Carlo simulations
  • Variational approximation
  • Prediction of future observations
  • Exercises
  • References
  • Summary
  • Chapter 4: Machine Learning Using Bayesian Inference
  • Why Bayesian inference for machine learning?
  • Model overfitting and bias-variance tradeoff
  • Selecting models of optimum complexity
  • Subset selection
  • Model regularization
  • Bayesian averaging
  • An overview of common machine learning tasks
  • References
  • Summary
  • Chapter 5: Bayesian Regression Models
  • Generalized linear regression
  • The arm package
  • The Energy efficiency dataset
  • Regression of energy efficiency with building parameters
  • Ordinary regression
  • Bayesian regression
  • Simulation of the posterior distribution
  • Exercises
  • References
  • Summary
  • Chapter 6: Bayesian Classification Models
  • Performance metrics for classification
  • The Naïve Bayes classifier
  • Text processing using the tm package
  • Model training and prediction
  • The Bayesian logistic regression model
  • The BayesLogit R package
  • The dataset
  • Preparation of the training and testing datasets
  • Using the Bayesian logistic model
  • Exercises
  • References
  • Summary
  • Chapter 7: Bayesian Models for Unsupervised Learning
  • Bayesian mixture models
  • The bgmm package for Bayesian mixture models
  • Topic modeling using Bayesian inference
  • Latent Dirichlet allocation
  • R packages for LDA
  • The topicmodels package
  • The lda package
  • Exercises
  • References
  • Summary
  • Chapter 8: Bayesian Neural Networks
  • Two-layer neural networks
  • Bayesian treatment of neural networks
  • The brnn R package
  • Deep belief networks and deep learning
  • Restricted Boltzmann machines
  • Deep belief networks
  • The darch R package
  • Other deep learning packages in R
  • Exercises
  • References
  • Summary
  • Chapter 9: Bayesian Modeling at Big Data Scale
  • Distributed computing using Hadoop
  • RHadoop for using Hadoop from R
  • Spark - in-memory distributed computing
  • SparkR
  • Linear regression using SparkR
  • Computing clusters on the cloud
  • Amazon Web Services
  • Creating and running computing instances on AWS
  • Installing R and RStudio
  • Running Spark on EC2
  • Microsoft Azure
  • IBM Bluemix
  • Other R packages for large scale machine learning
  • The parallel R package
  • The foreach R package
  • Exercises
  • References
  • Summary
  • Index

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