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Bayesian mixture models are widely used for clustering of high-dimensional data with appropriate uncertainty quantification.
Objective criteria for the evaluation of clustering methods
Rand, W. M. (1971) · 1971
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Objective criteria for the evaluation of clustering methods
Rand, W. M. (1971) · 1971
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A Bayesian analysis of some nonparametric problems
Ferguson, T. S. (1973) · 1973
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Bayesian cluster analysis
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Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
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Ockham’s razor and Bayesian analysis
Jefferys, W. H. and Berger, J. O. (1992) · 1992
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Hyperparameter estimation in Dirichlet process mixture models
West, M. (1992) · 1992
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Evaluating the accuracy of sampling-based approaches to the calculations of posterior moments
Geweke, J. (1992) · 1992
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Model-based Gaussian and non-Gaussian clustering
Banfield, J. D. and Raftery, A. E. (1993) · 1993
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A constructive definition of Dirichlet priors
Sethuraman, J. (1994) · 1994
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Bayesian density estimation and inference using mixtures
Escobar, M. D. and West, M. (1995) · 1995
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The EM algorithm for mixtures of factor analyzers
Ghahramani, Z., Hinton, G. E., · 1996
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The two-parameter Poisson-Dirichlet distribution derived from a stable subordinator
Pitman, J. and Yor, M. (1997) · 1997
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On Bayesian analysis of mixtures with an unknown number of components (with discussion)
Richardson, S. and Green, P. J. (1997) · 1997
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Gibbs sampling methods for stick-breaking priors
Ishwaran, H. and James, L. F. (2001) · 2001
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Distributed Bayesian clustering using finite mixture of mixtures
Song, H., Wang, Y., and Dunson, D. B. (2020) · 2003
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Some theory for Fisher’s linear discriminant function, ‘naive Bayes’, and some alternatives when there are many more variables than observations
Bickel, P. J. and Levina, E. (2004) · 2004
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A split-merge Markov chain Monte Carlo procedure for the Dirichlet process mixture model
Jain, S. and Neal, R. M. (2004) · 2004
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Augmented implicitly restarted Lanczos bidiagonalization methods
Baglama, J. and Reichel, L. (2005) · 2005
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Bayesian variable selection in clustering high-dimensional data
Tadesse, M. G., Sha, N., and Vannucci, M. (2005) · 2005
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Finite Mixture and Markov Switching Models
Frühwirth-Schnatter, S. (2006) · 2006
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Variable selection in clustering via Dirichlet process mixture models
Kim, S., Tadesse, M. G., and Vannucci, M. (2006) · 2006
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CODA: Convergence diagnosis and output analysis for MCMC
Plummer, M., Best, N., Cowles, K., and Vines, K. (2006) · 2006
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Finite mixture models are typically inconsistent for the number of components
Cai, D., Campbell, T., and Broderick, T. (2020) · 2007
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Large dimensional factor analysis
Bai, J. and Ng, S. (2008) · 2008
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High dimensional covariance matrix estimation using a factor model
Fan, J., Fan, Y., and Lv, J. (2008) · 2008
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Handling sparsity via the horseshoe
Carvalho, C. M., Polson, N. G., and Scott, J. G. (2009) · 2009
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Nonparametric Bayes local partition models for random effects
Dunson, D. B. (2009) · 2009
Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors
Li, H., Courtois, E. T., Sengupta, D., Tan, Y., Chen, K. H., Goh, J. J. L., Kong, S. L., Chua, C., Hon, L. K., Tan, W. S., · 2017
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M3Drop: Dropout-based feature selection for scRNASeq
Andrews, T. S. and Hemberg, M. (2018) · 2018
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Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., and Satija, R. (2018) · 2018
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Computational solutions for Bayesian inference in mixture models
Celeux, G., Kamary, K., Malsiner-Walli, G., Marin, J.-M., and Robert, C. P. (2018) · 2018
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UMAP: Uniform manifold approximation and projection for dimension reduction
McInnes, L., Healy, J., Saul, N., and Großberger, L. (2018) · 2018
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Penalized factor mixture analysis for variable selection in clustered data
Galimberti, G., Montanari, A., and Viroli, C. (2009) · 2009
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Mixtures of factor analyzers with common factor loadings: Applications to the clustering and visualization of high-dimensional data
Baek, J., McLachlan, G. J., and Flack, L. K. (2010) · 2010
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Heteroscedastic factor mixture analysis
Montanari, A. and Viroli, C. (2010) · 2010
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Sparse Bayesian infinite factor models
Bhattacharya, A. and Dunson, D. B. (2011) · 2011
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High-dimensional covariance matrix estimation in approximate factor models
Fan, J., Liao, Y., and Mincheva, M. (2011) · 2011
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Introduction to the non-asymptotic analysis of random matrices
Vershynin, R. (2012) · 2012
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Mixture models with a prior on the number of components
Miller, J. W. and Harrison, M. T. (2018) · 2018
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A single-cell sequencing guide for immunologists
See, P., Lum, J., Chen, J., and Ginhoux, F. (2018) · 2018
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Bayesian cluster analysis: Point estimation and credible balls (with discussion)
Wade, S. and Ghahramani, Z. (2018) · 2018
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UMAP: Uniform manifold approximation and projection for dimension reduction
McInnes, L., Healy, J., Saul, N., and Großberger, L. (2018) · 2018
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From here to infinity: Sparse finite versus Dirichlet process mixtures in model-based clustering
Frühwirth-Schnatter, S. and Malsiner-Walli, G. (2019) · 2019
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Handbook of mixture analysis
Fruhwirth-Schnatter, S., Celeux, G., and Robert, C. P. (2019) · 2019
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Challenges in unsupervised clustering of single-cell RNA-seq data
Kiselev, V. Y., Andrews, T. S., and Hemberg, M. (2019) · 2019
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Robust Bayesian inference via coarsening
Miller, J. W. and Dunson, D. B. (2019) · 2019
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IMIFA : Infinite Mixtures of Infinite Factor Analysers and Related Models
Murphy, K., Viroli, C., and Gormley, I. C. (2019) · 2019
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Bayesian cumulative shrinkage for infinite factorizations
Legramanti, S., Durante, D., and Dunson, D. B. (2020) · 2020
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Scalable Bayesian nonparametric clustering and classification
Ni, Y., Müller, P., Diesendruck, M., Williamson, S., Zhu, Y., and Ji, Y. (2020) · 2020
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Generalized mixtures of finite mixtures and telescoping sampling
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Clustering consistency with Dirichlet process mixtures
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Bayesian sparse Gaussian mixture model in high dimensions
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