On The Gaussian Approximation To Bayesian Posterior Distributions
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The present article derives the minimal number N of observations needed to consider a Bayesian posterior distribution as Gaussian. Two examples are presented. Within one of them, a chi-squared distribution, the observable x as well as the parameter ξ are defined all over the real axis, in the other one, the binomial distribution, the observable x is an entire number while the parameter ξ is defined on a finite interval of the real axis. The required minimal N is high in the first case and low for the binomial model. In both cases the precise definition of the measure μ on the scale of ξ is crucial.
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