On a noniformative prior distribution for bayesian inference of multinomial distribution's parameters

Summary

Noninformative prior distributions for Bayesian inference, for example, Jeffreys' priors, are much useful for the so-called “objective Bayesian inference” and make it possible to develop a method more powerful and flexible than traditional methods. In this paper a non-informative prior distribution, which is different from usual Jeffreys' priors, is introduced for Bayesian inference of multinomial distribution's parameters, using the assumption of the prior independence of the transformed parameters and the approximate data-translated likelihood function, and a short theoretical consideration for the inference based on the prior is attempted.