Label.switching

R package label.switching: Relabelling MCMC Outputs of Mixture Models. The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms.

Keywords for this software

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References in zbMATH (referenced in 20 articles , 1 standard article )

Showing results 1 to 20 of 20.
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  1. Kunkel, Deborah; Peruggia, Mario: Anchored Bayesian Gaussian mixture models (2020)
  2. Lin, Zhixiang; Zamanighomi, Mahdi; Daley, Timothy; Ma, Shining; Wong, Wing Hung: Model-based approach to the joint analysis of single-cell data on chromatin accessibility and gene expression (2020)
  3. Mollica, Cristina; Tardella, Luca: PLMIX: an R package for modelling and clustering partially ranked data (2020)
  4. Panagiotis Papastamoulis, Ioannis Ntzoufras: On the identifiability of Bayesian factor analytic models (2020) arXiv
  5. Papastamoulis, Panagiotis: Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components (2020)
  6. Aliverti, Emanuele; Durante, Daniele: Spatial modeling of brain connectivity data via latent distance models with nodes clustering (2019)
  7. David Ardia; Keven Bluteau; Kris Boudt; Leopoldo Catania; Denis-Alexandre Trottier: Markov-Switching GARCH Models in R: The MSGARCH Package (2019) not zbMATH
  8. Ranciati, Saverio; Galimberti, Giuliano; Soffritti, Gabriele: Bayesian variable selection in linear regression models with non-normal errors (2019)
  9. Sadeghianpourhamami, Nasrin; Benoit, Dries F.; Deschrijver, Dirk; Develder, Chris: Bayesian cylindrical data modeling using Abe-Ley mixtures (2019)
  10. Egidi, Leonardo; Pappadà, Roberta; Pauli, Francesco; Torelli, Nicola: Relabelling in Bayesian mixture models by pivotal units (2018)
  11. Okada, Kensuke; Mayekawa, Shin-ichi: Post-processing of Markov chain Monte Carlo output in Bayesian latent variable models with application to multidimensional scaling (2018)
  12. Papastamoulis, Panagiotis: Overfitting Bayesian mixtures of factor analyzers with an unknown number of components (2018)
  13. Vitelli, Valeria; Sørensen, Øystein; Crispino, Marta; Frigessi, Arnoldo; Arjas, Elja: Probabilistic preference learning with the Mallows rank model (2018)
  14. Mollica, Cristina; Tardella, Luca: Bayesian Plackett-Luce mixture models for partially ranked data (2017)
  15. Saptarshi Chakraborty, Samuel W.K. Wong: BAMBI: An R package for Fitting Bivariate Angular Mixture Models (2017) arXiv
  16. Cristina Mollica, Luca Tardella: PLMIX: An R package for modeling and clustering partially ranked data (2016) arXiv
  17. Lee, Jeong Eun; Robert, Christian P.: Importance sampling schemes for evidence approximation in mixture models (2016)
  18. Okada, Kensuke; Lee, Michael D.: A Bayesian approach to modeling group and individual differences in multidimensional scaling (2016)
  19. Panagiotis Papastamoulis, Magnus Rattray: BayesBinMix: an R Package for Model Based Clustering of Multivariate Binary Data (2016) arXiv
  20. Papastamoulis, P.: label.switching: An R Package for Dealing with the Label Switching Problem in MCMC Outputs (2016) not zbMATH