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The Bayesian Paradigm

Part of the Springer Texts in Statistics book series (STS)

In the previous chapters, we regarded the population quantities as unknown fixed constants and the observations or records as outcomes of a random process. In Chapter 3 the sampling process and in Chapters 1 and 2 the data-generating process, as described by a model equation or a class of joint distributions, were the sole sources of randomness. This chapter introduces a radically different approach in which the observed quantities (data) are fixed and all unknown quantities are random and described by their joint posterior distribution-the conditional distribution of the target given what is known.

Keywords

Posterior Distribution Monte Carlo Markov Chain Prior Distribution Bayesian Analysis Conditional Distribution 
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.

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Copyright information

© Springer 2008

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