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You're reading from Learning Bayesian Models with R

Product typeBook

Published inOct 2015

Reading LevelBeginner

PublisherPackt

ISBN-139781783987603

Edition1st Edition

Languages

Concepts

Machine Learning

Author (1)

Hari Manassery Koduvely

Summary

To summarize this chapter, we discussed elements of probability theory; particularly those aspects required for learning Bayesian inference. Due to lack of space, we have not covered many elementary aspects of this subject. There are some excellent books on this subject, for example, books by William Feller (reference 2 in the References section of this chapter), E. T. Jaynes (reference 3 in the References section of this chapter), and M. Radziwill (reference 4 in the References section of this chapter). Readers are encouraged to read these to get a more in-depth understanding of probability theory and how it can be applied in real-life situations.

In the next chapter, we will introduce the R programming language that is the most popular open source framework for data analysis and Bayesian inference in particular.

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Learning Bayesian Models with R

Published in: Oct 2015Publisher: PacktISBN-13: 9781783987603

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Author (1)

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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