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Igor Prünster
Researcher at Bocconi University
Publications - 110
Citations - 3460
Igor Prünster is an academic researcher from Bocconi University. The author has contributed to research in topics: Dirichlet process & Prior probability. The author has an hindex of 29, co-authored 106 publications receiving 3033 citations. Previous affiliations of Igor Prünster include Instituto Tecnológico Autónomo de México & University of Turin.
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Journal ArticleDOI
Distributional results for means of normalized random measures with independent increments
TL;DR: In this paper, the authors consider the problem of determining the distribution of means of random probability measures which are obtained by normalizing increasing additive processes and find a solution by resorting to a well-known inversion formula for characteristic functions due to Gurland.
Book ChapterDOI
Models beyond the Dirichlet process
Antonio Lijoi,Igor Prünster +1 more
TL;DR: In this paper, the authors provide a review of Bayesian nonparametric models that go beyond the Dirichlet process, and show that in some cases of interest for statistical applications, the DPM is not an adequate prior choice.
Journal ArticleDOI
Controlling the reinforcement in Bayesian non-parametric mixture models
TL;DR: A Bayesian non‐parametric approach is taken and adopt a hierarchical model with a suitable non-parametric prior obtained from a generalized gamma process to solve the problem of determining the number of components in a mixture model.
Journal ArticleDOI
Posterior Analysis for Normalized Random Measures with Independent Increments
TL;DR: A comprehensive Bayesian non‐parametric analysis of random probabilities which are obtained by normalizing random measures with independent increments (NRMI), which allows to derive a generalized Blackwell–MacQueen sampling scheme, which is then adapted to cover also mixture models driven by general NRMIs.
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Hierarchical Mixture Modeling With Normalized Inverse-Gaussian Priors
TL;DR: In this article, the normalized inverse-Gaussian (N-IG) prior is proposed as an alternative to the Dirichlet process to be used in Bayesian hierarchical models.