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.

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.

This book constitutes the thoroughly refereed post-proceedings of the International Workshop on Statistical Network Analysis: Models, Issues, and New Directions held in Pittsburgh, PA, USA in June 2006 as associated event of the 23th International Conference on Machine Learning, ICML 2006.

The 12 revised full papers and 4 invited lectures presented together with the summary of the closing panel discussion were carefully revised and selected during two rounds of reviewing and improvement from the presentations at the workshop. The papers focus on probabilistic methods for network analysis, paying special attention to model design and computational issues of learning and inference. The workshop brings together statistical network modeling researchers from different communities to create and motivate novel modeling approaches, diverse applications, and new research directions.

A Survey of Statistical Network Models aims to provide the reader with an entry point to the voluminous literature on statistical network modeling. It guides the reader through the development of key stochastic network models, touches upon a number of examples and commonalities across different parts of the network literature, and discusses major schools of thought in static and dynamic network modeling. Networks have found a prominent place in our everyday lives. In science, networks have been used to analyze interpersonal social relationships, communication, academic paper co- authorships and citations, protein interaction patterns, and much more. Popular books on networks and their analysis began to appear a decade ago, and online networking communities such as Facebook, MySpace, and LinkedIn now include millions of people from around the world. Formal statistical modeling for the analysis of network data has emerged as a major research topic in diverse areas of study. A Survey of Statistical Network Models aims to provide the reader with an entry point to the voluminous literature on statistical network modeling.It guides the reader through the development of key stochastic network models, touches upon a number of examples and commonalities across different parts of the network literature, and discusses major schools of thought in static and dynamic network modeling. In addition it illuminates the interconnections between existing models. Despite the rich and extensive network modeling literature, many statistical questions remain unanswered. It is hoped that the concluding discussion of gaps and challenges will help the interested reader deduce important future research directions.