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- W51088069 abstract "This chapter is discusses the random effects models. These models describe at least a two-stage hierarchical process, where first subpopulations are sampled and then observations are made within each selected subpopulation. Traditionally, analysis of such models has been restricted to inferences about variance components: one component to describe variation within subpopulations, and one or more component for variation among subpopulations. There are several attractions to considering Bayesian inferences for random effects. Bayesian analysis treats all unknown parameters as random variables. Thus, the distinction between fixed and random effects is less fundamental in a Bayesian than in a sampling theory framework. Earlier Bayesian writers emphasized that in the random effects model information from other sampled subpopulations—collateral information—has the potential of greatly reducing the impact of prior specifications on posterior inferences for a given subpopulation, even when the sample available for that subpopulation is small." @default.
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- W51088069 date "1989-01-01" @default.
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- W51088069 title "DIFFICULTIES WITH BAYESIAN INFERENCE FOR RANDOM EFFECTS" @default.
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- W51088069 doi "https://doi.org/10.1016/b978-0-12-108840-8.50008-1" @default.
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