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What Does Objective Mean in a Dirichlet†multinomial Process?

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  • Danilo Alvares
  • Carmen Armero
  • Anabel Forte

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.

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  • Danilo Alvares & Carmen Armero & Anabel Forte, 2018. "What Does Objective Mean in a Dirichlet†multinomial Process?," International Statistical Review, International Statistical Institute, vol. 86(1), pages 106-118, April.
    • Handle: RePEc:bla:istatr:v:86:y:2018:i:1:p:106-118
    DOI: 10.1111/insr.12231
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    Cited by:

    1. Jan Povala & Seppo Virtanen & Mark Girolami, 2020. "Burglary in London: insights from statistical heterogeneous spatial point processes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1067-1090, November.

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