Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Learning Bayesian Models with R
  • Toc
  • feedback
Learning Bayesian Models with R

Learning Bayesian Models with R

By : Hari Manassery Koduvely
3.4 (7)
close
Learning Bayesian Models with R

Learning Bayesian Models with R

3.4 (7)
By: Hari Manassery Koduvely

Overview of this book

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.
Table of Contents (11 chapters)
close
10
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...

bookmark search playlist font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete