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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
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. 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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