Handbook of mixed membership models and their applications / [edited by] Edoardo M. Airoldi ... [et al.].

Includes bibliographical references and indexes.

Part 1 Mixed Membership: Setting the Stage;
1 Introduction to Mixed Membership Models and Methods;
2 A Tale of Two ( Types of) Memberships: Comparing Mixed and Partial Membership with a Continuous Data Example;
3 Interpreting Mixed Membership Models: Implications of Erosheva's Representation Theorem;
4 A Simple and General Exponential Family Framework for Partial Membership and Factor Analysis;
5 Nonparametric Mixed Membership Models

Part 2 The Grade of Membership Model and Its Extensions
6 A Mixed Membership Approach to the Assessment of Political Ideology from Survey Responses;
7 Estimating Diagnostic Error without a Gold Standard: A Mixed Membership Approach;
8 Interpretability Constraints and Trade- offs in Using Mixed Membership Models;
9 Mixed Membership Trajectory Models;
10 An Analysis of Development of Dementia through the Extended Trajectory Grade of Membership Model;

Part 3 Topic Models: Mixed Membership Models for Text
11 Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference;
12 Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements;
13 Block- LDA: Jointly Modeling Entity- Annotated Text and Entity- Entity Links;
14 Robust Estimation of Topic Summaries Leveraging Word Frequency and Exclusivity;

Part 4 Semi- Supervised Mixed Membership Models;
15 Mixed Membership Classification for Documents with Hierarchically Structured Labels;
16 Discriminative Mixed Membership Models;
17 Mixed Membership Matrix Factorization;
18 Discriminative Training of Mixed Membership Models;

Part 5 Special Methodology for Sequence and Rank Data;
19 Population Stratification with Mixed Membership Models;
20 Mixed Membership Models for Time Series;
21 Mixed Membership Models for Rank Data: Investigating Structure in Irish Voting Data;

Part 6 Mixed Membership Models for Networks;
22 Hierarchical Mixed Membership Stochastic Blockmodels;
23 Analyzing Time- Evolving Networks
24 Mixed Membership Blockmodels for Dynamic Networks with Feedback;
25 Overlapping Clustering Methods for Networks;
Subject index--
Author index.

This book shows 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.