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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

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Product type Paperback
Published in Oct 2015
Publisher Packt
ISBN-13 9781783987603
Length 168 pages
Edition 1st Edition
Languages
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Author (1):
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Hari Manassery Koduvely Hari Manassery Koduvely
Author Profile Icon Hari Manassery Koduvely
Hari Manassery Koduvely
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Table of Contents (11) Chapters Close

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


  1. Almeida T.A., Gómez Hidalgo J.M., and Yamakami A. "Contributions to the Study of SMS Spam Filtering: New Collection and Results". In: 2011 ACM Symposium on Document Engineering (DOCENG'11). Mountain View, CA, USA. 2011

  2. MacKay D.J.C. "The Evidence Framework Applied to Classification Networks". Neural Computation 4(5)

  3. "Bayesian Inference for Logistic Models Using Pólya-Gamma Latent Variables". Journal of the American Statistical Association. Volume 108, Issue 504, Page 1339. 2013

  4. Costello D.A.E., Little M.A., McSharry P.E., Moroz I.M., and Roberts S.J. "Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection". BioMedical Engineering OnLine. 2007

  5. Filannino M. "DBWorld e-mail Classification Using a Very Small Corpus". Project of Machine Learning Course. University of Manchester. 2011

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