Some Characteristics of Bayesian Designs

Klaus Felsenstein
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Klaus Felsenstein: Technische Universität Wien

A chapter in Probability and Bayesian Statistics, 1987, pp 169-174 from Springer

Abstract: Abstract A considerable number of stochastic models comprise the potentiality of selecting the experimental conditions. A control-variable influences the observations and likewise the gained information about some parameter or in a more Bayesian mode of expression the ‘state of nature’. Reaching our goal of increasing the information demands a model-formulation with independence between the parameter and the chosen control variable or with a concrete functional connection that seems defendable. The choice of an appropriate likelihood is aggraviated by specifying how the distribution of the observations is altered by different levels of the control variable. An even more difficult problem is the valuation of information and precision. Each measure of information has to stand many discussions about its shortcomings and hardly any can be employed generally.

Keywords: Prior Distribution; Linear Regression Model; Prior Density; Location Family; Design Versus (search for similar items in EconPapers)
Date: 1987
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DOI: 10.1007/978-1-4613-1885-9_17

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