Handbook of Mixed Membership Models and Their Applications
1st Edition
Published on 31. March 2021
Book
Paperback/Softback
620 pages
978-0-367-33084-2 (ISBN)
Description
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.
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.
Reviews / Votes
"The editors of this volume have notably worked at the forefront of research in various subfields of mixed membershi modeling since the field's inception. . . . The strength of their collaboration in editing this volume can be seen in the book's organization. . . .One of the main strengths of the book, fulfilling the promise of its title, is the wealth of applications described therein . . . We believe this book sets the stage for a rich body of future work."~Trevor Campbell and Tamara Broderick, Massachusetts Institute of Technology "The editors of this volume have notably worked at the forefront of research in various subfields of mixed membershi modeling since the field's inception. . . . The strength of their collaboration in editing this volume can be seen in the book's organization. . . .One of the main strengths of the book, fulfilling the promise of its title, is the wealth of applications described therein . . . We believe this book sets the stage for a rich body of future work."
~Trevor Campbell and Tamara Broderick, Massachusetts Institute of Technology
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Researchers, practitioners, and graduate students in statistics, biological sciences, and computer science.
Illustrations
143 farbige Abbildungen, 61 s/w Tabellen
61 Tables, black and white; 143 Illustrations, color
Dimensions
Height: 254 mm
Width: 178 mm
Weight
1150 gr
ISBN-13
978-0-367-33084-2 (9780367330842)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Edoardo M. Airoldi | David Blei | Elena A. Erosheva
Handbook of Mixed Membership Models and Their Applications
E-Book
11/2014
1st Edition
Chapman & Hall/CRC
€100.99
Available for download
Edoardo M. Airoldi | David Blei | Elena A. Erosheva
Handbook of Mixed Membership Models and Their Applications
Book
11/2014
1st Edition
CRC Press
€267.50
Shipment within 15-20 days
Edoardo M. Airoldi | David Blei | Elena A. Erosheva
Handbook of Mixed Membership Models and Their Applications
E-Book
11/2014
1st Edition
Chapman and Hall
€100.99
Available for download
Persons
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.
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.
Content
Mixed Membership: Setting the Stage. The Grade of Membership Model and Its Extensions. Topic Models: Mixed Membership Models for Text. Semi-Supervised Mixed Membership Models. Special Methodology for Sequence and Rank Data. Mixed Membership Models for Networks. Index.