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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

Product type Paperback

Published in Oct 2015

Publisher Packt

ISBN-13 9781783987603

Length 168 pages

Edition 1st Edition

Languages

R

Concepts

Machine Learning

Author (1):

Hari Manassery Koduvely

View More author details

Table of Contents (11) Chapters

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

References

http://en.wikipedia.org/wiki/List_of_probability_distributions
Feller W. An Introduction to Probability Theory and Its Applications. Vol. 1. Wiley Series in Probability and Mathematical Statistics. 1968. ISBN-10: 0471257087
Jayes E.T. Probability Theory: The Logic of Science. Cambridge University Press. 2003. ISBN-10: 0521592712
Radziwill N.M. Statistics (The Easier Way) with R: an informal text on applied statistics. Lapis Lucera. 2015. ISBN-10: 0692339426

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Learning Bayesian Models with R

Published in: Oct 2015

Publisher: Packt

ISBN-13: 9781783987603

© 2015 Packt Publishing Limited All Rights Reserved

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Authors (1)

Hari Manassery Koduvely

Hari Manassery Koduvely

Dr. Hari M. Koduvely is an experienced data scientist working at the Samsung R&D Institute in Bangalore, India. He has a PhD in statistical physics from the Tata Institute of Fundamental Research, Mumbai, India, and post-doctoral experience from the Weizmann Institute, Israel, and Georgia Tech, USA. Prior to joining Samsung, the author has worked for Amazon and Infosys Technologies, developing machine learning-based applications for their products and platforms. He also has several publications on Bayesian inference and its applications in areas such as recommendation systems and predictive health monitoring. His current interest is in developing large-scale machine learning methods, particularly for natural language understanding.

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