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Learning Bayesian Models with R

更新時間：2021-07-09 21:22:51

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Thisbookisforstatisticians,analysts,anddatascientistswhowanttobuildaBayes-basedsystemwithRandimplementitintheirday-to-daymodelsandprojects.ItismainlyintendedforDataScientistsandSoftwareEngineerswhoareinvolvedinthedevelopmentofAdvancedAnalyticsapplications.Tounderstandthisbook,itwouldbeusefulifyouhavebasicknowledgeofprobabilitytheoryandanalyticsandsomefamiliaritywiththeprogramminglanguageR.

目錄(88章)

倒序

coverpage
Learning Bayesian Models with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Support files eBooks discount offers and more
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Chapter 1. Introducing the Probability Theory
Probability distributions
Conditional probability
Bayesian theorem
Marginal distribution
Expectations and covariance
Exercises
References
Summary
Chapter 2. The R Environment
Setting up the R environment and packages
Managing data in R
Writing R programs
Data visualization
Sampling
Exercises
References
Summary
Chapter 3. Introducing Bayesian Inference
Bayesian view of uncertainty
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
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
Simulation of the posterior distribution
Exercises
References
Summary
Chapter 6. Bayesian Classification Models
Performance metrics for classification
The Na?ve Bayes classifier
The Bayesian logistic regression model
Exercises
References
Summary
Chapter 7. Bayesian Models for Unsupervised Learning
Bayesian mixture models
Topic modeling using Bayesian inference
R packages for LDA
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
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
Other R packages for large scale machine learning
Exercises
References
Summary
Index 更新時間：2021-07-09 21:22:51

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Learning Bayesian Models with R