Literature: Finite Mixtures

Papers on identifiability, estimation, and learning of finite mixture models. This area covers classical mixture identifiability, Gaussian mixture recovery, nonparametric and grouped-sample identifiability, latent-class and topic models, and conditional-mixture/mixture-of-experts identifiability.

Closely related project note: Background on identification and learning of mixture distributions. Candidate papers to ingest are already cited by name in that background note (Teicher 1963, Yakowitz-Spragins 1968, Allman-Matias-Rhodes 2009, Kalai-Moitra-Valiant 2010, Moitra-Valiant 2010, Hsu-Kakade 2013, Vandermeulen-Scott 2019, Ashtiani et al. 2020, and others).

Papers

No entries yet.