Handbook of Mixed Membership Models and Their Applications

Edoardo M. Airoldi, David Blei, Elena A. Erosheva, Stephen E. Fienberg

November 6, 2014 by Chapman and Hall/CRC
Reference - 618 Pages - 143 Color
ISBN 9781466504080 - CAT# K14507
Series: Chapman & Hall/CRC Handbooks of Modern Statistical Methods

USD$119.95

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Features

  • Chronicles recent practical and theoretical developments in mixed membership modeling
  • Explains how mixed membership models are used to analyze complex multivariate data in document collections, network data, social and health sciences applications, and population genetics
  • Discusses how to mitigate issues with more complicated mixed membership models

Summary

In response to scientific needs for more diverse and structured explanations of statistical data, researchers have discovered how to model individual data points as belonging to multiple groups. Handbook of Mixed Membership Models and Their Applications shows you how to use these flexible modeling tools to uncover hidden patterns in modern high-dimensional multivariate data. It explores the use of the models in various application settings, including survey data, population genetics, text analysis, image processing and annotation, and molecular biology.

Through examples using real data sets, you’ll discover how to characterize complex multivariate data in:

  • Studies involving genetic databases
  • Patterns in the progression of diseases and disabilities
  • Combinations of topics covered by text documents
  • Political ideology or electorate voting patterns
  • Heterogeneous relationships in networks, and much more

The handbook spans more than 20 years of the editors’ and contributors’ statistical work in the field. Top researchers compare partial and mixed membership models, explain how to interpret mixed membership, delve into factor analysis, and describe nonparametric mixed membership models. They also present extensions of the mixed membership model for text analysis, sequence and rank data, and network data as well as semi-supervised mixed membership models.