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You're reading from  Learning Bayesian Models with R

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Published inOct 2015
Reading LevelBeginner
PublisherPackt
ISBN-139781783987603
Edition1st Edition
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Hari Manassery Koduvely
Hari Manassery Koduvely
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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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The Energy efficiency dataset


We will use the Energy efficiency dataset from the UCI Machine Learning repository for the illustration of Bayesian regression (reference 2 in the References section of this chapter). The dataset can be downloaded from the website at http://archive.ics.uci.edu/ml/datasets/Energy+efficiency. The dataset contains the measurements of energy efficiency of buildings with different building parameters. There are two energy efficiency parameters measured: heating load (Y1) and cooling load (Y2).

The building parameters used are: relative compactness (X1), surface area (X2), wall area (X3), roof area (X4), overall height (X5), orientation (X6), glazing area (X7), and glazing area distribution (X8). We will try to predict heating load as a function of all the building parameters using both ordinary regression and Bayesian regression, using the glm functions of the arm package. We will show that, for the same dataset, Bayesian regression gives significantly smaller prediction...

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Learning Bayesian Models with R
Published in: Oct 2015Publisher: PacktISBN-13: 9781783987603

Author (1)

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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.
Read more about Hari Manassery Koduvely