Statistical Information and Likelihood

In part one of this essay the notion of ‘statistical information generated by a data’ is formulated in terms of some intuitively appealing principles of data analysis. The author comes out very strongly in favour of the unrestricted likelihood principle after demonstrating (to his own satisfaction) the reasonableness of the Bayes-Fisher postulate that, within the framework of a particular statistical model, the ‘whole of the relevant information in the data’ must be supposed to be summarised in the likelihood function generated by the data.

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