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Learning Bayesian Models with R

You're reading from   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

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Product type Paperback
Published in Oct 2015
Publisher Packt
ISBN-13 9781783987603
Length 168 pages
Edition 1st Edition
Languages
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Author (1):
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Hari Manassery Koduvely Hari Manassery Koduvely
Author Profile Icon Hari Manassery Koduvely
Hari Manassery Koduvely
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Table of Contents (11) Chapters Close

Preface 1. Introducing the Probability Theory FREE CHAPTER 2. The R Environment 3. Introducing Bayesian Inference 4. Machine Learning Using Bayesian Inference 5. Bayesian Regression Models 6. Bayesian Classification Models 7. Bayesian Models for Unsupervised Learning 8. Bayesian Neural Networks 9. Bayesian Modeling at Big Data Scale Index

Data visualization

One of the powerful features of R is its functions for generating high-quality plots and visualize data. The graphics functions in R can be divided into three groups:

  • High-level plotting functions to create new plots, add axes, labels, and titles.
  • Low-level plotting functions to add more information to an existing plot. This includes adding extra points, lines, and labels.
  • Interactive graphics functions to interactively add information to, or extract information from, an existing plot.

The R base package itself contains several graphics functions. For more advanced graph applications, one can use packages such as ggplot2, grid, or lattice. In particular, ggplot2 is very useful for generating visually appealing, multilayered graphs. It is based on the concept of grammar of graphics. Due to lack of space, we are not covering these packages in this book. Interested readers should consult the book by Hadley Wickham (reference 4 in the References section of this chapter).

High...

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