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Martin J. Wainwright | Michael I. Jordan | Peter L. Bartlett | Nhat Ho | Wenlong Mou | P. Bartlett | M. Wainwright | Nhat Ho | Wenlong Mou
[1] J. Cheeger. A lower bound for the smallest eigenvalue of the Laplacian , 1969 .
[2] P. Buser. A note on the isoperimetric constant , 1982 .
[3] M. Talagrand,et al. Probability in Banach Spaces: Isoperimetry and Processes , 1991 .
[4] Miklós Simonovits,et al. Random Walks in a Convex Body and an Improved Volume Algorithm , 1993, Random Struct. Algorithms.
[5] M. Ledoux. A simple analytic proof of an inequality by P. Buser , 1994 .
[6] R. Tweedie,et al. Exponential convergence of Langevin distributions and their discrete approximations , 1996 .
[7] Jon A. Wellner,et al. Weak Convergence and Empirical Processes: With Applications to Statistics , 1996 .
[8] P. Donnelly,et al. Inferring coalescence times from DNA sequence data. , 1997, Genetics.
[9] S. MacEachern,et al. Estimating mixture of dirichlet process models , 1998 .
[10] S. Bobkov. Isoperimetric and Analytic Inequalities for Log-Concave Probability Measures , 1999 .
[11] J. Ghosh,et al. POSTERIOR CONSISTENCY OF DIRICHLET MIXTURES IN DENSITY ESTIMATION , 1999 .
[12] Radford M. Neal. Markov Chain Sampling Methods for Dirichlet Process Mixture Models , 2000 .
[13] M. Stephens. Bayesian analysis of mixture models with an unknown number of components- an alternative to reversible jump methods , 2000 .
[14] M. Stephens. Dealing with label switching in mixture models , 2000 .
[15] C. Robert,et al. Computational and Inferential Difficulties with Mixture Posterior Distributions , 2000 .
[16] Lancelot F. James,et al. Bayesian Model Selection in Finite Mixtures by Marginal Density Decompositions , 2001 .
[17] A. V. D. Vaart,et al. Entropies and rates of convergence for maximum likelihood and Bayes estimation for mixtures of normal densities , 2001 .
[18] D. Balding,et al. Approximate Bayesian computation in population genetics. , 2002, Genetics.
[19] Santosh S. Vempala,et al. Logconcave functions: geometry and efficient sampling algorithms , 2003, 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings..
[20] Radford M. Neal,et al. A Split-Merge Markov chain Monte Carlo Procedure for the Dirichlet Process Mixture Model , 2004 .
[21] Galin L. Jones,et al. Sufficient burn-in for Gibbs samplers for a hierarchical random effects model , 2004, math/0406454.
[22] A. Bovier,et al. Metastability in Reversible Diffusion Processes I: Sharp Asymptotics for Capacities and Exit Times , 2004 .
[23] Ajay Jasra,et al. Markov Chain Monte Carlo Methods and the Label Switching Problem in Bayesian Mixture Modeling , 2005 .
[24] Michael I. Jordan,et al. Hierarchical Dirichlet Processes , 2006 .
[25] Maria G. Reznikoff,et al. A new criterion for the logarithmic Sobolev inequality and two applications , 2007 .
[26] A. Belloni,et al. On the Computational Complexity of MCMC-Based Estimators in Large Samples , 2007, 0704.2167.
[27] A. V. D. Vaart,et al. Posterior convergence rates of Dirichlet mixtures at smooth densities , 2007, 0708.1885.
[28] A. Gelfand,et al. The Nested Dirichlet Process , 2008 .
[29] T. Lelièvre. A general two-scale criteria for logarithmic Sobolev inequalities , 2009 .
[30] Maria G. Westdickenberg,et al. A two-scale approach to logarithmic Sobolev inequalities and the hydrodynamic limit , 2009 .
[31] Santosh S. Vempala,et al. Sampling s-Concave Functions: The Limit of Convexity Based Isoperimetry , 2009, APPROX-RANDOM.
[32] S. Sharma,et al. The Fokker-Planck Equation , 2010 .
[33] Stephen G. Walker,et al. Slice sampling mixture models , 2011, Stat. Comput..
[34] J. Rosenthal,et al. Convergence rate of Markov chain methods for genomic motif discovery , 2013, 1303.2814.
[35] X. Nguyen. Convergence of latent mixing measures in finite and infinite mixture models , 2011, 1109.3250.
[36] Martin J. Wainwright,et al. Statistical guarantees for the EM algorithm: From population to sample-based analysis , 2014, ArXiv.
[37] A. Dalalyan. Theoretical guarantees for approximate sampling from smooth and log‐concave densities , 2014, 1412.7392.
[38] Martin J. Wainwright,et al. On the Computational Complexity of High-Dimensional Bayesian Variable Selection , 2015, ArXiv.
[39] Gersende Fort,et al. A Shrinkage-Thresholding Metropolis Adjusted Langevin Algorithm for Bayesian Variable Selection , 2013, IEEE Journal of Selected Topics in Signal Processing.
[40] Arian Maleki,et al. Global Analysis of Expectation Maximization for Mixtures of Two Gaussians , 2016, NIPS.
[41] Matus Telgarsky,et al. Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis , 2017, COLT.
[42] Santosh S. Vempala,et al. Algorithmic Theory of ODEs and Sampling from Well-conditioned Logconcave Densities , 2018, ArXiv.
[43] Jeffrey W. Miller,et al. Mixture Models With a Prior on the Number of Components , 2015, Journal of the American Statistical Association.
[44] Michael I. Jordan,et al. Underdamped Langevin MCMC: A non-asymptotic analysis , 2017, COLT.
[45] Ohad Shamir,et al. Global Non-convex Optimization with Discretized Diffusions , 2018, NeurIPS.
[46] Nisheeth K. Vishnoi,et al. Dimensionally Tight Running Time Bounds for Second-Order Hamiltonian Monte Carlo , 2018, ArXiv.
[47] Andrej Risteski,et al. Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition , 2018, ArXiv.
[48] Andrej Risteski,et al. Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo , 2017, NeurIPS.
[49] Michael I. Jordan,et al. Sharp Convergence Rates for Langevin Dynamics in the Nonconvex Setting , 2018, ArXiv.
[50] Martin J. Wainwright,et al. Log-concave sampling: Metropolis-Hastings algorithms are fast! , 2018, COLT.
[51] Michael I. Jordan,et al. Sampling can be faster than optimization , 2018, Proceedings of the National Academy of Sciences.
[52] David B. Dunson,et al. Robust Bayesian Inference via Coarsening , 2015, Journal of the American Statistical Association.
[53] Soumendu Sundar Mukherjee,et al. Weak convergence and empirical processes , 2019 .
[54] A. Bhattacharya,et al. Bayesian fractional posteriors , 2016, The Annals of Statistics.
[55] Nhat Ho,et al. On posterior contraction of parameters and interpretability in Bayesian mixture modeling , 2019, Bernoulli.
[56] G. A. Young,et al. High‐dimensional Statistics: A Non‐asymptotic Viewpoint, Martin J.Wainwright, Cambridge University Press, 2019, xvii 552 pages, £57.99, hardback ISBN: 978‐1‐1084‐9802‐9 , 2020, International Statistical Review.
[57] A. Eberle,et al. Coupling and convergence for Hamiltonian Monte Carlo , 2018, The Annals of Applied Probability.