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.
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