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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

Chapter 4. Machine Learning Using Bayesian Inference

Now that we have learned about Bayesian inference and R, it is time to use both for machine learning. In this chapter, we will give an overview of different machine learning techniques and discuss each of them in detail in subsequent chapters. Machine learning is a field at the intersection of computer science and statistics, and a subbranch of artificial intelligence or AI. The name essentially comes from the early works in AI where researchers were trying to develop learning machines that automatically learned the relationship between input and output variables from data alone. Once a machine is trained on a dataset for a given problem, it can be used as a black box to predict values of output variables for new values of input variables.

It is useful to set this learning process of a machine in a mathematical framework. Let X and Y be two random variables such that we seek a learning machine that learns the relationship between...

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