What Does Objective Mean in a Dirichlet‐multinomial Process? |
| |
| Authors: | Danilo Alvares Carmen Armero Anabel Forte |
| |
| Affiliation: | University of Valencia, Valencia, Spain |
| |
| Abstract: | The Dirichlet‐multinomial process can be seen as the generalisation of the binomial model with beta prior distribution when the number of categories is larger than two. In such a scenario, setting informative prior distributions when the number of categories is great becomes difficult, so the need for an objective approach arises. However, what does objective mean in the Dirichlet‐multinomial process? To deal with this question, we study the sensitivity of the posterior distribution to the choice of an objective Dirichlet prior from those presented in the available literature. We illustrate the impact of the selection of the prior distribution in several scenarios and discuss the most sensible ones. |
| |
| Keywords: | Bayesian inference non‐informative priors posterior mean |
|
|
