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Bayesian Statistics

  • Thomas Haslwanter
Chapter
Part of the Statistics and Computing book series (SCO)

Abstract

Bayesian Statistics, a technique that has become very popular for many types of machine learning, starts out with a new view at statistical data: it takes the observed data as fixed, and looks at the likelihood to find certain model parameters. This chapter introduces Bayesian Statistics, and provides a worked example using the Python package “PyMC,” showing how Bayesian Statistics can provide more information than classical statistical modeling.

Keywords

Bayesian Statistics Modeling Gaussian Process Prior Odds Python Package Frequentist Interpretation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Bishop, C. M. (2007). Pattern recognition and machine learning. New York: Springer.Google Scholar
  2. Duda, R. O., Hart, P. E., & Stork, D. G. (2004). Pattern classification (2nd ed.). Hoboken: Wiley-Interscience.Google Scholar
  3. Pilon, C. D. (2015). Probabilistic programming and Bayesian methods for hackers. http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/ Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Thomas Haslwanter
    • 1
  1. 1.School of Applied Health and Social SciencesUniversity of Applied Sciences Upper AustriaLinzAustria

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