 | Handbook of Mixed Membership Models and Their Applications Subjects: Behavioral Sciences; Engineering & Technology; Mathematics & Statistics; Psychological Science; Systems & Control Engineering; Statistics & Probability; Psychological Methods & Statistics; Machine Learning; Statistics; 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-dim 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. |
