What is it about?
The uniform prior probability density may deliver information and lead to inconsistent Bayesian inference. We reinvestigated the problem, avoiding delivering unrecognised information and looking at it in a novel way.
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Why is it important?
Avoiding improper prior densities opens the way to selecting among different models competing to explain the data.
Perspectives
Readers will learn how to analyse the problems, while realizing the basic principles and concepts, logical justifications, assumptions on which solutions are based, and the level of approximation entailed.
Dr Giovanni Mana
Istituto Nazionale di Ricerca Metrologica
Read the Original
This page is a summary of: Bayesian inference of the mean power of several Gaussian data, Zeitschrift für Physik B Condensed Matter, June 2024, Springer Science + Business Media,
DOI: 10.1140/epjb/s10051-024-00737-w.
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