A new R package for Bayesian estimation of multivariate normal mixtures allowing for selection of the number of components and interval-censored data

DOI10.1016/j.csda.2009.05.006zbMath1453.62020OpenAlexW2037337949WikidataQ57655367 ScholiaQ57655367MaRDI QIDQ961883

Arnošt Komárek

Publication date: 1 April 2010

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

Full work available at URL: https://doi.org/10.1016/j.csda.2009.05.006

Mathematics Subject Classification ID

Software, source code, etc. for problems pertaining to statistics (62-04) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Bayesian inference (62F15)

Related Items (9)

Flexible modelling of survival curves for censored data ⋮ Mixture models applied to heterogeneous populations ⋮ Bayesian hierarchical mixture models for detecting non‐normal clusters applied to noisy genomic and environmental datasets ⋮ Univariate Bayesian nonparametric mixture modeling with unimodal kernels ⋮ SMLSOM: the shrinking maximum likelihood self-organizing map ⋮ Clustering for multivariate continuous and discrete longitudinal data ⋮ Bayesian analysis of skew-normal independent linear mixed models with heterogeneity in the random-effects population ⋮ mixAK ⋮ Is infinity that far? A Bayesian nonparametric perspective of finite mixture models

Uses Software

• R
• CODA
• WinBUGS

Cites Work

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• Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
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• Penalized loss functions for Bayesian model comparison
• Population-Based Reversible Jump Markov Chain Monte Carlo
• The Calculation of Posterior Distributions by Data Augmentation
• Statistical analysis of finite mixture distributions
• Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models
• Dealing With Label Switching in Mixture Models
• Finite mixture models
• Computational and Inferential Difficulties with Mixture Posterior Distributions
• Bayesian Measures of Model Complexity and Fit
• Reversible Jump, Birth-and-Death and More General Continuous Time Markov Chain Monte Carlo Samplers
• Bayes Factors
• Transdimensional Markov Chains
• Deviance information criteria for missing data models

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