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- W206469751 abstract "This chapter discusses the various complexities that exist in the use of probabilities in applied situations. The most prominent of these has long been recognized and concerns conditional probability given an event having zero probability. A partition is needed to define the necessary limiting process. At another extreme is the bayesian claim that probabilities can describe all unknowns. The chapter explores a complexity that falls somewhere between these and that is concerned with a parameter that becomes redundant if symmetrical error is used; information that specifies the value of the symmetry parameter may produce different results if applied formally to the conditional distribution describing the realization or if used realistically to modify the model. The complexity examined in the chapter concerns a multidimensional parameter and the effect of information that changes the dimension of the parameter. The complexity arises with a probability space model, and it arises in the corresponding bayesian analysis based on the conventional right invariant prior. The complexity is resolved for the probability space model. Its resolution for bayesian theory would presumably need to wait until there is a bayesian substantiation of the use of the right invariant prior." @default.
- W206469751 created "2016-06-24" @default.
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- W206469751 date "1973-01-01" @default.
- W206469751 modified "2023-09-25" @default.
- W206469751 title "Inference and Redundant Parameters" @default.
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- W206469751 doi "https://doi.org/10.1016/b978-0-12-426653-7.50015-6" @default.
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