1997
DOI: 10.1016/s0167-7152(97)00050-3
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A note on the Dirichlet process prior in Bayesian nonparametric inference with partial exchangeability

Abstract: We consider Bayesian nonparametric inference for continuous-valued partially exchangeable data, when the partition of the observations into groups is unknown. This includes change-point problems and mixture models. As the prior, we consider a mixture of products of Dirichlet processes. We show that the discreteness of the Dirichlet process can have a large effect on inference (posterior distributions and Bayes factors), leading to conclusions that can be different from those that result from a reasonable param… Show more

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Cited by 32 publications
(30 citation statements)
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“…A similar concern was expressed in Petrone & Raftery (1997) with particular reference to change point models. We can summarize equality of allocation by the Fig.…”
Section: Entropy and Partitionsmentioning
confidence: 87%
“…A similar concern was expressed in Petrone & Raftery (1997) with particular reference to change point models. We can summarize equality of allocation by the Fig.…”
Section: Entropy and Partitionsmentioning
confidence: 87%
“…9] and Refs. [45][46][47][48][49][50]. A common characteristic of these methods is that they are rather technical, and that the resulting priors are hard to interpret from a behavioural point of view.…”
Section: Discussionmentioning
confidence: 99%
“…Mira and Petrone [15] also by the application of the Gibbs sampler algorithm approximated the posterior distribution in the above model. This can therefore be shown that, when α 1 (.…”
Section: Simple Change Pointmentioning
confidence: 99%