Bayesian Methods and Mixture Models

TopicLeading institutions, researchers & key papers

This cluster of papers focuses on the application of mixture models, particularly Gaussian finite mixture models and Dirichlet process mixture models, for model-based clustering, discriminant analysis, density estimation, and unsupervised learning. It explores various inference methods such as Bayesian inference, variational inference, and Markov Chain Monte Carlo for estimating parameters in mixture models. The cluster also delves into the challenges of identifiability, variable selection, and dealing with label switching in the context of mixture models.

Works

IDs:OpenAlex

How has Bayesian Methods and Mixture Models's publication output changed over time?

ScholarIQpublication output · 1995–2022

Output grew100% over the shown period — from 1 works in 1995 to 2 in 2022.

1995200220042010201320182019202020212022

What are the most-cited papers on Bayesian Methods and Mixture Models?

ScholarIQmost cited works

Cubic splines to model relationships between continuous variables and outcomes: a guide for clinicians

Jordon Gauthier, Qian Wu, Ted Gooley

S183182149. 2019490 CitationsOPEN ACCESS

Bayesian inference in threshold models using Gibbs sampling

D. Sorensen, S. B. Andersen, Daniel Gianola, Inge Riis Korsgaard

S184365670. 1995371 CitationsOPEN ACCESS

Bayesian Statistical Modelling

Stephen J. Ganocy

S985303. 2002240 Citations

Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark

Grégoire Preud’homme, Kévin Duarte, Kévin Dalleau, Claire Lacomblez, Emmanuel Bresso, Malika Smaïl‐Tabbone, Miguel Couceiro, Marie‐Dominique Devignes, Masatake Kobayashi, Olivier Huttin, João Pedro Ferreira, Faı̈ez Zannad, Patrick Rossignol, Nicolas Girerd

Scientific Reports. 2021111 CitationsOPEN ACCESS

CHIME: Clustering of high-dimensional Gaussian mixtures with EM algorithm and its optimality

Tianxi Cai, Jing Ma, Linjun Zhang

S119757635. 201970 CitationsOPEN ACCESS