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Learning Bayesian Models with R

You're reading from  Learning Bayesian Models with R

Product type Book
Published in Oct 2015
Publisher Packt
ISBN-13 9781783987603
Pages 168 pages
Edition 1st Edition
Languages
Author (1):
Hari Manassery Koduvely Hari Manassery Koduvely
Profile icon Hari Manassery Koduvely

Table of Contents (16) Chapters

Learning Bayesian Models with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Introducing the Probability Theory 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

Exercises


  1. Use the multivariate dataset named Auto MPG from the UCI Machine Learning repository (reference 3 in the References section of this chapter). The dataset can be downloaded from the website at https://archive.ics.uci.edu/ml/datasets/Auto+MPG. The dataset describes automobile fuel consumption in miles per gallon (mpg) for cars running in American cities. From the folder containing the datasets, download two files: auto-mpg.data and auto-mpg.names. The auto-mpg.data file contains the data and it is in space-separated format. The auto-mpg.names file has several details about the dataset, including variable names for each column. Build a regression model for the fuel efficiency, as a function displacement (disp), horse power (hp), weight (wt), and acceleration (accel), using both OLS and Bayesian GLM. Predict the values for mpg in the test dataset using both the OLS model and Bayesian GLM model (using the bayesglm function). Find the Root Mean Square Error (RMSE) values for OLS and...

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