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Dirichlet Process mixture models (DPMM) in combination with Gaussian kernels have been an important modeling tool for numerous data domains arising from biological, physical, and social sciences.
Autogmm: Automatic gaussian mixture modeling in python
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Chakravarti, P., Balakrishnan, S., and Wasserman, L · 1910
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On a class of bayesian nonparametric estimates : I. density estimates
Lo, A · 1984
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Mixture models: Inference and Applications to Clustering
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Model based gaussian and non-gaussian clustering
Banfield, J. and Raftery, A · 1993
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Fitting a mixture model by expectation maximization to discover motifs in bipolymers
Bailey, T. L., Elkan, C., et al · 1994
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Bayesian density estimation and inference using mixtures
Escobar, M. and West, M · 1995
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Mixture models: Theory, Geometry and Applications
Lindsay, B · 1995
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Probability inequalities for likelihood ratios and convergences of sieves mles
Wong, W. H. and Shen, X · 1995
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Mixtures of distributions: inference and estimation. In Markov Chain Monte Carlo in Practice (W. Gilks, S. Richardson and D. Spiegelhalter, eds.)
Robert, C · 1996
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Practical bayesian density estimation using mixtures of normals
Roeder, K. and Wasserman, L · 1997
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Estimating mixture of dirichlet process models
MacEachern, S. and Muller, P · 1998
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The consistency of posterior distributions in nonparametric problems
Barron, A., Schervish, M., and Wasserman, L · 1999
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Dependent nonparametric processes
MacEachern, S · 1999
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Convergence rates of posterior distributions
Ghosal, S., Ghosh, J. K., and van der Vaart, A · 2000
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Entropies and rates of convergence for maximum likelihood and bayes estimation for mixtures of normal densities
Ghosal, S. and van der Vaart, A · 2001
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Modelling heterogeneity with and without the dirichlet process
Green, P. and Richardson, S · 2001
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The Laplace distribution and generalizations
Kotz, S., Kozubowski, T. J., and Podgorski, K · 2001
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Estimating cure rates from survival data
Tsodikov, A. D., Ibrahim, J. G., and Yakovlev, A. Y · 2003
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Topics in Optimal Transportation
Villani, C · 2003
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Hierarchical clustering of a mixture model
Goldberger, J. and Roweis, S · 2004
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Applications of beta-mixture models in bioinformatics
Ji, Y., Wu, C., Liu, P., Wang, J., and Coombes, K. R · 2005
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A study of gaussian mixture models of color and texture features for image classification and segmentation
Permuter, H., Francos, J., and Jermyn, I · 2006
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Are gibbs-type priors the most natural generalization of the dirichlet process?
De Blasi, P., Favaro, S., Lijoi, A., Mena, R. H., Pruenster, I., and Ruggiero, M · 2015
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Posterior contraction rates for deconvolution of dirichlet-laplace mixtures
Gao, F. and van der Vaart, A. W · 2016
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Stochastic Optimization for Large-scale Optimal Transport
Genevay, A., Cuturi, M., Peyré, G., and Bach, F · 2016
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Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
Altschuler, J., Niles-Weed, J., and Rigollet, P · 2017
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Large-scale optimal transport and mapping estimation
Seguy, V., Damodaran, B. B., Flamary, R., Courty, N., Rolet, A., and Blondel, M · 2017
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Convergence rates of posterior distributions for noniid observations
Ghosal, S. and van der Vaart, A · 2007
Cited alongside, same era.
Optimal bayesian estimation of gaussian mixtures with growing number of components
Ohn, I. and Lin, L · 2007
Cited alongside, same era.
Medical Applications of Finite Mixture Models
Schlattmann, P · 2009
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Optimal Transport: Old and New. Grundlehren der Mathematischen Wissenschaften [Fundamental Principles of Mathemtical Sciences]
Villani, C · 2009
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Complex Analysis
Stein, E. and Shakarchi, R · 2010
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Mixtures: Estimation and Applications
Mengersen, K. L., Robert, C., and Titterington, M · 2011
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Wellner, J. A · 2017
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Bayesian repulsive Gaussian mixture model
Xie, F. and Xu, Y · 2017
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Hypothesis testing of the q-matrix
Gu, Y., Liu, J., Xu, G., and Ying, Z · 2018
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Strong identifiability and optimal minimax rates for finite mixture estimation
Heinrich, P. and Kahn, J · 2018
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Mixture models with a prior on the number of components
Miller, J. W. and Harrison, M. T · 2018
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Entropy-Regularized Optimal Transport for Machine Learning. (Régularisation Entropique du Transport Optimal pour le Machine Learning)
Genevay, A · 2019
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On efficient optimal transport: An analysis of greedy and accelerated mirror descent algorithms
Lin, T., Ho, N., and Jordan, M · 2019
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On posterior contraction of parameters and interpretability in bayesian mixture modeling
Guha, A., Ho, N., and Nguyen., X · 2021
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Estimating the number of components in finite mixture models via the Group-Sort-Fuse procedure
Manole, T. and Khalili, A · 2021
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Egmm: An evidential version of the gaussian mixture model for clustering
Jiao, L. et al · 2022
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On the efficiency of entropic regularized algorithms for optimal transport
Lin, T., Ho, N., and Jordan, M. I · 2022
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Refined convergence rates for maximum likelihood estimation under finite mixture models
Manole, T. and Ho, N · 2022
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