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.


References in zbMATH (referenced in 27 articles , 1 standard article )

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  1. Chai, Christine P.: Word distinctivity -- quantifying improvement of topic modeling results from (n)-gramming (2022)
  2. Papastamoulis, Panagiotis; Ntzoufras, Ioannis: On the identifiability of Bayesian factor analytic models (2022)
  3. Yamaguchi, Kazuhiro; Templin, Jonathan: A Gibbs sampling algorithm with monotonicity constraints for diagnostic classification models (2022)
  4. Hadj-Amar, Beniamino; Finkenstädt, Bärbel; Fiecas, Mark; Huckstepp, Robert: Identifying the recurrence of sleep apnea using a harmonic hidden Markov model (2021)
  5. Im, Yunju; Tan, Aixin: Bayesian subgroup analysis in regression using mixture models (2021)
  6. Osmundsen, Kjartan Kloster; Kleppe, Tore Selland; Oglend, Atle: MCMC for Markov-switching models -- Gibbs sampling vs. marginalized likelihood (2021)
  7. Sergio Venturini, Raffaella Piccarreta : A Bayesian Approach for Model-Based Clustering of Several Binary Dissimilarity Matrices: The dmbc Package in R (2021) not zbMATH
  8. Kunkel, Deborah; Peruggia, Mario: Anchored Bayesian Gaussian mixture models (2020)
  9. 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)
  10. Mollica, Cristina; Tardella, Luca: PLMIX: an R package for modelling and clustering partially ranked data (2020)
  11. Panagiotis Papastamoulis, Ioannis Ntzoufras: On the identifiability of Bayesian factor analytic models (2020) arXiv
  12. Papastamoulis, Panagiotis: Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components (2020)
  13. Aliverti, Emanuele; Durante, Daniele: Spatial modeling of brain connectivity data via latent distance models with nodes clustering (2019)
  14. David Ardia; Keven Bluteau; Kris Boudt; Leopoldo Catania; Denis-Alexandre Trottier: Markov-Switching GARCH Models in R: The MSGARCH Package (2019) not zbMATH
  15. Ranciati, Saverio; Galimberti, Giuliano; Soffritti, Gabriele: Bayesian variable selection in linear regression models with non-normal errors (2019)
  16. Sadeghianpourhamami, Nasrin; Benoit, Dries F.; Deschrijver, Dirk; Develder, Chris: Bayesian cylindrical data modeling using Abe-Ley mixtures (2019)
  17. Egidi, Leonardo; Pappadà, Roberta; Pauli, Francesco; Torelli, Nicola: Relabelling in Bayesian mixture models by pivotal units (2018)
  18. Okada, Kensuke; Mayekawa, Shin-ichi: Post-processing of Markov chain Monte Carlo output in Bayesian latent variable models with application to multidimensional scaling (2018)
  19. Papastamoulis, Panagiotis: Overfitting Bayesian mixtures of factor analyzers with an unknown number of components (2018)
  20. Vitelli, Valeria; Sørensen, Øystein; Crispino, Marta; Frigessi, Arnoldo; Arjas, Elja: Probabilistic preference learning with the Mallows rank model (2018)

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