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About this product
- PublisherTaylor & Francis Inc
- Date of Publication01/12/2014
- Series TitleChapman & Hall/CRC Handbooks of Modern Statistical Methods
- Place of PublicationBosa Roca
- Country of PublicationUnited States
- ImprintCRC Press Inc
- Content Note143 colour illustrations, 61 black & white tables
- Weight1474 g
- Width178 mm
- Height254 mm
- Edited byDavid Blei,Edoardo M. Airoldi,Elena A. Erosheva,Stephen E. Fienberg
- Format DetailsUnsewn / adhesive bound
- Table Of ContentsMixed Membership: Setting the Stage Introduction to Mixed Membership Models and Methods Edoardo M. Airoldi, David M. Blei, Elena A. Erosheva, and Stephen E. Fienberg A Tale of Two (Types of) Memberships Jonathan Gruhl and Elena A. Erosheva Interpreting Mixed Membership April Galyardt Partial Membership and Factor Analysis Zoubin Ghahramani, Shakir Mohamed, and Katherine Heller Nonparametric 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-Vallier Estimating Diagnostic Error without a Gold Standard Elena A. Erosheva and Cyrille Joutard Interpretability of Mixed Membership Models Burton H. Singer and Marcia C. Castro Mixed Membership Trajectory Models Daniel Manrique-Vallier Analysis 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. Jordan Care and Feeding of Topic Models Jordan Boyd-Graber, David Mimno, and David Newman Block-LDA: Jointly Modeling Entity-Annotated Text and Entity-Entity Links Ramnath Balasubramanyan and William W. Cohen Robust 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 Perotte Discriminative Mixed Membership Models Hanhuai Shan and Arindam Banerjee Mixed Membership Matrix Factorization Lester Mackey, David Weiss, and Michael I. Jordan Discriminative 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. Xing Mixed Membership Models for Time Series Emily B. Fox and Michael I. Jordan Mixed 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. Junker Analyzing Time-Evolving Networks Qirong Ho and Eric P. Xing Mixed Membership Blockmodels for Dynamic Networks with Feedback Yoon-Sik Cho, Greg Ver Steeg, and Aram Galstyan Overlapping Clustering Methods for Networks Pierre Latouche, Etienne Birmele, and Christophe Ambroise Subject Index Author Index References appear at the end of each chapter.
- Author BiographyEdoardo 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.
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