Handbook of Mixed Membership Models and Their Applications

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.

Mixed Membership: Setting the Stage Introduction to Mixed Membership Models and Methods Edoardo M. Airoldi, David M. Blei, Elena A. Erosheva, and Stephen E. FienbergA Tale of Two (Types of) Memberships Jonathan Gruhl and Elena A. EroshevaInterpreting Mixed Membership April GalyardtPartial Membership and Factor Analysis Zoubin Ghahramani, Shakir Mohamed, and Katherine HellerNonparametric Mixed Membership Models Daniel Heinz The Grade of Membership Model and Its Extensions A Mixed Membership Approach to Political Ideology Justin H. Gross and Daniel Manrique-VallierEstimating Diagnostic Error without a Gold Standard Elena A. Erosheva and Cyrille JoutardInterpretability of Mixed Membership Models Burton H. Singer and Marcia C. CastroMixed Membership Trajectory Models Daniel Manrique-VallierAnalysis of Development of Dementia through the Extended TGoM Model Fabrizio Lecci Topic Models: Mixed Membership Models for Text Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference John Paisley, David M. Blei, and Michael I. JordanCare and Feeding of Topic Models Jordan Boyd-Graber, David Mimno, and David NewmanBlock-LDA: Jointly Modeling Entity-Annotated Text and Entity-Entity Links Ramnath Balasubramanyan and William W. CohenRobust Estimation of Topic Summaries Leveraging Word Frequency and Exclusivity Jonathan M. Bischof and Edoardo M. Airoldi Semi-Supervised Mixed Membership Models Mixed Membership Classification for Documents with Hierarchically Structured Labels Frank Wood and Adler PerotteDiscriminative Mixed Membership Models Hanhuai Shan and Arindam BanerjeeMixed Membership Matrix Factorization Lester Mackey, David Weiss, and Michael I. JordanDiscriminative Training of Mixed Membership Models Jun Zhu and Eric P. Xing Special Methodology for Sequence and Rank Data Population Stratification with Mixed Membership Models Suyash Shringarpure and Eric P. XingMixed Membership Models for Time Series Emily B. Fox and Michael I. JordanMixed Membership Models for Rank Data Isobel Claire Gormley and Thomas Brendan Murphy Mixed Membership Models for Networks Hierarchical Mixed Membership Stochastic Blockmodels Tracy M. Sweet, Andrew C. Thomas, and Brian W. JunkerAnalyzing Time-Evolving Networks Qirong Ho and Eric P. XingMixed Membership Blockmodels for Dynamic Networks with Feedback Yoon-Sik Cho, Greg Ver Steeg, and Aram GalstyanOverlapping Clustering Methods for Networks Pierre Latouche, Etienne Birmelé, and Christophe Ambroise Subject Index Author Index References appear at the end of each chapter.

Edoardo M. Airoldi is an associate professor of statistics at Harvard University. Dr. Airoldi’s current research focuses on statistical theory and methods for designing and analyzing experiments in the presence of network interference as well as on modeling and inferential issues when dealing with network data. David M. Blei is a professor of statistics and computer science at Columbia University. Dr. Blei’s research is in statistical machine learning involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference. Elena A. Erosheva is an associate professor of statistics and social work at the University of Washington, where she is a core member of the Center for Statistics and the Social Sciences. Dr. Erosheva’s research focuses on the development and application of modern statistical methods to address important issues in the social, medical, and health sciences. Stephen E. Fienberg is the Maurice Falk University Professor of Statistics and Social Science at Carnegie Mellon University, where he is co-director of the Living Analytics Research Centre and a member of the Department of Statistics, the Machine Learning Department, the Heinz College, and Cylab. Dr. Fienberg’s research includes the development of statistical methods for categorical data analysis and network data analysis.