On posterior contraction of parameters and interpretability in Bayesian mixture modeling

15 January 2019

Papers citing "On posterior contraction of parameters and interpretability in Bayesian mixture modeling"

10 / 10 papers shown

Title
Model-free Estimation of Latent Structure via Multiscale Nonparametric Maximum Likelihood Bryon Aragam Ruiyi Yang 45 0 0 29 Oct 2024
Bayesian mixture models (in)consistency for the number of clusters Louise Alamichel D. Bystrova Julyan Arbel Guillaume Kon Kam King 42 5 0 25 Oct 2022
Consistency of mixture models with a prior on the number of components Jeffrey W. Miller 25 71 0 06 May 2022
Selective inference for k-means clustering Yiqun T. Chen Daniela Witten 25 43 0 29 Mar 2022
Beyond Black Box Densities: Parameter Learning for the Deviated Components Dat Do Nhat Ho X. Nguyen 16 2 0 05 Feb 2022
MCMC computations for Bayesian mixture models using repulsive point processes Mario Beraha R. Argiento Jesper Møller A. Guglielmi 13 17 0 12 Nov 2020
Optimal Bayesian estimation of Gaussian mixtures with growing number of components Ilsang Ohn Lizhen Lin 31 17 0 17 Jul 2020
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes Mengdi Xu Wenhao Ding Jiacheng Zhu Zuxin Liu Baiming Chen Ding Zhao CLL OffRL 21 34 0 19 Jun 2020
Estimating the Number of Components in Finite Mixture Models via the Group-Sort-Fuse Procedure Tudor Manole Abbas Khalili 27 19 0 24 May 2020
Robust estimation of mixing measures in finite mixture models Nhat Ho X. Nguyen Yaácov Ritov 29 9 0 23 Sep 2017