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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. Derive the equation for the posterior mean by expanding the square in the exponential for each i, collecting all similar power terms, and making a perfect square again. Note that the product of exponentials can be written as the exponential of a sum of terms.
  2. For this exercise, we use the dataset corresponding to Smartphone-Based Recognition of Human Activities and Postural Transitions, from the UCI Machine Learning repository (https://archive.ics.uci.edu/ml/datasets/Smartphone-Based+Recognition+of+Human+Activities+and+Postural+Transitions). It contains values of acceleration taken from an accelerometer on a smartphone. The original dataset contains x, y, and z components of the acceleration and the corresponding timestamp values. For this exercise, we have used only the two horizontal components of the acceleration x and y. In this exercise, let's assume that the acceleration follows a normal distribution. Let's also assume a normal prior distribution for the mean values of...

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