Dependent mixture models: clustering and borrowing information

DOI10.1016/j.csda.2013.06.015zbMath1471.62114OpenAlexW2027714503MaRDI QIDQ1621321

Antonio Lijoi, Igor Prünster, Bernardo Nipoti

Publication date: 8 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://dem-web.unipv.it/web/docs/dipeco/quad/ps/RePEc/pav/demwpp/DEMWP0046.pdf

zbMATH Keywords

Bayesian nonparametricsDirichlet processmixture modelsgeneralized Pólya urn schemenormalized \(\sigma\)-stable processpartially exchangeable random partitiondependent process

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Random measures (60G57) Nonparametric inference (62G99)

Related Items (13)

Modelling and computation using NCoRM mixtures for density regression ⋮ Importance conditional sampling for Pitman-Yor mixtures ⋮ Random density functions with common atoms and pairwise dependence ⋮ Dependent mixtures of geometric weights priors ⋮ On the inferential implications of decreasing weight structures in mixture models ⋮ The semi-hierarchical Dirichlet process and its application to clustering homogeneous distributions ⋮ System identification using autoregressive Bayesian neural networks with nonparametric noise models ⋮ Sampling hierarchies of discrete random structures ⋮ Distribution theory for hierarchical processes ⋮ Discussion of ``On simulation and properties of the stable law by L. Devroye and L. James ⋮ Thinned completely random measures with applications in competing risks models ⋮ A multivariate extension of a vector of two-parameter Poisson–Dirichlet processes ⋮ A class of dependent Dirichlet processes via latent multinomial processes

Uses Software

• DPpackage

Cites Work

• Unnamed Item
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• Bayesian Nonparametrics
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• A Method for Combining Inference Across Related Nonparametric Bayesian Models
• Bayesian Density Estimation and Inference Using Mixtures
• Controlling the Reinforcement in Bayesian Non-Parametric Mixture Models
• Hierarchical Mixture Modeling With Normalized Inverse-Gaussian Priors

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