Nonparametric Mixed Membership Models

Authored by: Edoardo M. Airoldi , David M. Blei , Elena A. Erosheva , Stephen E. Fienberg , Daniel Heinz

Handbook of Mixed Membership Models and Their Applications

Print publication date:  November  2014
Online publication date:  November  2014

Print ISBN: 9781466504080
eBook ISBN: 9781466504097
Adobe ISBN:

10.1201/b17520-7

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Abstract

One issue with parametric latent class models, regardless of whether or not they feature mixed memberships, is the need to specify a bounded number of classes a priori. By contrast, nonparametric models use an unbounded number of classes, of which some random number are observed in the data. In this way, nonparametric models provide a method to infer the correct number of classes based on the number of observations and their similarity.

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