Abstract
Parametric and non-parametric Bayesian predictions are compared here through their posterior predictive distributions. Those distributions are computed from Student samples generated by simulation. The respective roles of the sample size and of the specification error in the quality of the posterior prediction are analysed, with the aim to propose some help in the choice of the method. The specification error is investigated by considering Normality of the data as parametric assump- tion. The non-parametric reasoning is based on a Dirichlet process as prior specification.
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