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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

The Energy efficiency dataset


We will use the Energy efficiency dataset from the UCI Machine Learning repository for the illustration of Bayesian regression (reference 2 in the References section of this chapter). The dataset can be downloaded from the website at http://archive.ics.uci.edu/ml/datasets/Energy+efficiency. The dataset contains the measurements of energy efficiency of buildings with different building parameters. There are two energy efficiency parameters measured: heating load (Y1) and cooling load (Y2).

The building parameters used are: relative compactness (X1), surface area (X2), wall area (X3), roof area (X4), overall height (X5), orientation (X6), glazing area (X7), and glazing area distribution (X8). We will try to predict heating load as a function of all the building parameters using both ordinary regression and Bayesian regression, using the glm functions of the arm package. We will show that, for the same dataset, Bayesian regression gives significantly smaller prediction...

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