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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
Concepts
Author (1):
Hari Manassery Koduvely Hari Manassery Koduvely
Profile icon Hari Manassery Koduvely
LinkedIn
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.
See other products by Hari Manassery Koduvely

Table of Contents (16) Chapters

Learning Bayesian Models with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Introducing the Probability Theory The R Environment Introducing Bayesian Inference Machine Learning Using Bayesian Inference Bayesian Regression Models Bayesian Classification Models Bayesian Models for Unsupervised Learning Bayesian Neural Networks Bayesian Modeling at Big Data Scale Index

References

  1. "MapReduce Implementation of Variational Bayesian Probabilistic Matrix Factorization Algorithm". In: IEEE Conference on Big Data. pp 145-152. 2013

  2. Dean J. and Ghemawat S. "MapReduce: Simplified Data Processing on Large Clusters". Communications of the ACM 51 (1). 107-113

  3. https://github.com/jeffreybreen/tutorial-rmr2-airline/blob/master/R/1-wordcount.R

  4. Chowdhury M., Das T., Dave A., Franklin M.J., Ma J., McCauley M., Shenker S., Stoica I., and Zaharia M. "Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing". NSDI 2012. 2012

  5. Amazon Elastic Compute Cloud (EC2) User Guide, Kindle e-book by Amazon Web Services, updated April 9, 2014

  6. Spark documentation for AWS at http://spark.apache.org/docs/latest/ec2-scripts.html

  7. AWS documentation for Spark at http://aws.amazon.com/elasticmapreduce/details/spark/

  8. Microsoft Virtual Academy website at http://www.microsoftvirtualacademy.com/training-courses/getting-started-with-microsoft-azure-machine-learning...

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Authors (1)

Profile icon Hari Manassery Koduvely
LinkedIn
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.
Read more
See other products by Hari Manassery Koduvely

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