Is infinity that far? A Bayesian nonparametric perspective of finite mixture models

DOI10.1214/22-AOS2201OpenAlexW2937361190MaRDI QIDQ2105188

Maria De Iorio, Raffaele Argiento

Publication date: 8 December 2022

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1904.09733

zbMATH Keywords

Dirichlet processMarkov chain Monte Carlo methodsBayesian clusteringBayesian mixture modelsmixture of finite mixtures

Mathematics Subject Classification ID

Density estimation (62G07) Bayesian inference (62F15) Monte Carlo methods (65C05)

Related Items (3)

Full uncertainty analysis for Bayesian nonparametric mixture models ⋮ Capture-Recapture Models with Heterogeneous Temporary Emigration ⋮ Is infinity that far? A Bayesian nonparametric perspective of finite mixture models

Uses Software

• mixAK
• STRUCTURE

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Model-based clustering based on sparse finite Gaussian mixtures
• Asymptotic Behaviour of the Posterior Distribution in Overfitted Mixture Models
• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
• Generalized mixtures of finite mixtures and telescoping sampling
• Random variate generation for Laguerre-type exponentially tilted \(\alpha\)-stable distributions
• Slice sampling mixture models
• A new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of components and interval-censored data
• On the posterior distribution of the number of components in a finite mixture
• Bayesian analysis of mixture models with an unknown number of components\,--\,an alternative to reversible jump methods.
• Convergence of latent mixing measures in finite and infinite mixture models
• On posterior contraction of parameters and interpretability in Bayesian mixture modeling
• How many data clusters are in the galaxy data set? Bayesian cluster analysis in action
• Is infinity that far? A Bayesian nonparametric perspective of finite mixture models
• From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering
• Finite mixture and Markov switching models.
• Hierarchical Normalized Completely Random Measures to Cluster Grouped Data
• Density Estimation With Confidence Sets Exemplified by Superclusters and Voids in the Galaxies
• Posterior Analysis for Normalized Random Measures with Independent Increments
• Model choice in generalised linear models: a Bayesian approach via Kullback-Leibler projections
• Random variate generation for exponentially and polynomially tilted stable distributions
• Sampling Exponentially Tilted Stable Distributions
• Combinatorial Methods in Discrete Distributions
• Mixture Models With a Prior on the Number of Components
• Gibbs Sampling Methods for Stick-Breaking Priors
• Bayesian Density Estimation and Inference Using Mixtures
• Handbook of Mixture Analysis
• An Introduction to the Theory of Point Processes
• The representation of 𝑒^{-𝑥^{𝜆}} as a Laplace integral
• MCMC for normalized random measure mixture models

This page was built for publication: Is infinity that far? A Bayesian nonparametric perspective of finite mixture models