On Bayesian learning from Bernoulli observations

Details On Bayesian learning from Bernoulli observations On Bayesian learning from Bernoulli observations

2009-02-16

arxiv.org/abs/0902.2544v2

Mathematics

Statistics Theory

This is a personal 19-pages manuscript version of the article that has been accepted for publication by Journal of Statistical

Scientific paper

10.1016/j.jspi.2010.05.023

We provide a reason for Bayesian updating, in the Bernoulli case, even when it is assumed that observations are independent and identically distributed with a fixed but unknown parameter $\theta_0$. The motivation relies on the use of loss functions and asymptotics. Such a justification is important due to the recent interest and focus on Bayesian consistency which indeed assumes that the observations are independent and identically distributed rather than being conditionally independent with joint distribution depending on the choice of prior.

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