What can we learn from posterior distributions?

A frequentist interpretation of Bayesian posterior

Assume we have observed N independent and identically distributed (iid) samples X = (x1, … , xN) from an unknown distribution q. A typical question in statistics is “what does the set of samples X tell us about the distribution q?”.

Parametric statistical methods assume that q belongs to a parametric family of distributions and that there exists a parameter θ where q(x) is equal to the parametric distribution p(x|θ) for all …

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