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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


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  2. Bishop C.M. Pattern Recognition and Machine Learning. Springer. 2006

  3. Biecek P., Szczurek E., Tiuryn J., and Vingron M. "The R Package bgmm: Mixture Modeling with Uncertain Knowledge". Journal of Statistical Software. Volume 47, Issue 3. 2012

  4. Bruno B., Mastrogiovanni F., Sgorbissa A., Vernazza T., and Zaccaria R. "Analysis of human behavior recognition algorithms based on acceleration data". In: IEEE Int Conf on Robotics and Automation (ICRA), pp. 1602-1607. 2013

  5. Bruno B., Mastrogiovanni F., Sgorbissa A., Vernazza T., and Zaccaria R. "Human Motion Modeling and Recognition: A computational approach". In: IEEE International Conference on Automation Science and Engineering (CASE). pp 156-161. 2012

  6. Hofmann T. "Probabilistic Latent Semantic Indexing". In: Twenty-Second Annual International SIGIR Conference....

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