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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

Writing R programs

Although much data analysis in R can be carried out in an interactive manner using command prompt, often for more complex tasks, one needs to write R scripts. As mentioned in the introduction, R has both the perspective of a functional and object-oriented programming language. In this section, some of the standard syntaxes of the programming in R are described.

Control structures

Control structures are meant for controlling the flow of execution of a program. The standard control structures are as follows:

  • if and else: To test a condition
  • for: To loop over a set of statements for a fixed number of times
  • while: To loop over a set of statements while a condition is true
  • repeat: To execute an infinite loop
  • break: To break the execution of a loop
  • next: To skip an iteration of a loop
  • return: To exit a function

Functions

If one wants to use R for more serious programming, it is essential to know how to write functions. They make the language more powerful and elegant. R has many built...

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