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Basic Ideas of Bayesian Methods

  • Bing LiEmail author
  • G. Jogesh Babu
Chapter
  • 2k Downloads
Part of the Springer Texts in Statistics book series (STS)

Abstract

This chapter is devoted to some basic ideas on the Bayesian approach to statistical inference, where the parameter is treated as a random variable, which is assigned a distribution. This distribution – the prior distribution – represents the prior knowledge about the parameter before observing the data. Once the data is observed, the inference about parameter is drawn from the posterior distribution – the conditional distribution of parameter given the data. The term “Bayesian” comes from the well-known Bayes theorem, which is a formula for computing the posterior probabilities.

References

  1. Berger, J. O. (1985). Statistical decision theory and Bayesian analysis. Second edition. Springer, New York.Google Scholar
  2. Lee, P. M. (2012). Bayesian Statistics: An Introduction, Fourth Edition. Wiley.Google Scholar
  3. Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979). Multivariate Analysis. Academic.Google Scholar
  4. O’Hagan, A. (1994). Kendall’s Advanced Theory of Statistics: Bayesian Inference, Volume 2B. Edward Arnold.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of StatisticsPenn State UniversityUniversity ParkUSA

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