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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2008 Springer
About this chapter
Cite this chapter
(2008). The Bayesian Paradigm. In: Studying Human Populations. Springer Texts in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-73251-0_4
Download citation
DOI: https://doi.org/10.1007/978-0-387-73251-0_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-98735-4
Online ISBN: 978-0-387-73251-0
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)