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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

References


  1. Friedman J., Hastie T., and Tibshirani R. The Elements of Statistical Learning – Data Mining, Inference, and Prediction. Springer Series in Statistics. 2009

  2. Tsanas A. and Xifara A. "Accurate Quantitative Estimation of Energy Performance of Residential Buildings Using Statistical Machine Learning Tools". Energy and Buildings. Vol. 49, pp. 560-567. 2012

  3. Quinlan R. "Combining Instance-based and Model-based Learning". In: Tenth International Conference of Machine Learning. 236-243. University of Massachusetts, Amherst. Morgan Kaufmann. 1993. Original dataset is from StatLib library maintained by Carnegie Mellon University.

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