On Consistent and Calibrated Inference about the Parameters of Sampling Distributions

Details On Consistent and Calibrated Inference about the Parameters of Sampling Distributions On Consistent and Calibrated Inference about the Parameters of Sampling Distributions

2005-08-02

arxiv.org/abs/physics/0508017v1

Physics

Data Analysis, Statistics and Probability

88 pages, 5 figures

Scientific paper

The theory of probability, based on very general rules referred to as the Cox-Polya-Jaynes Desiderata, can be used both as a theory of random mass phenomena and as a quantitative theory of plausible inference about the parameters of sampling distributions. The existing applications of the Desiderata must be extended in order to allow for consistent inferences in the limit of complete a priori ignorance about the values of the parameters. Since the limits of consistent quantitative inference from incomplete information can clearly be established, the developed theory is necessarily an effective one. It is interesting to note that when applying the Desiderata strictly, we find no contradictions between the so-called Bayesian and frequentist schools of inductive reasoning.

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