The Identification of Discrete Mixture Models

Author: Gordon, Spencer Lane

Year: 2023

Degree: Dissertation (Ph.D.)

Advisors: Vidick, Thomas G.; Schulman, Leonard J.

Committee Members: Yue, Yisong; Vidick, Thomas G.; Schulman, Leonard J.; Wierman, Adam C.

Option: Computer Science

DOI: 10.7907/ebf5-0b48

Abstract

In this thesis we discuss a variety of results on learning and identifying discrete mixture models, i.e., distributions that are a convex combination of k from a known class C of distributions. We first consider the case where C is the class of binomial distributions, before generalizing to the case of product distributions. We provide a necessary condition for identifiability of mixture of products distributions as well as a generalization to structured mixtures over multiple latent variables.

Files