Name: Handbook of Mixed Membership Models and Their Applications (en Inglés)
SKU: 55638523
Price: 1556.14 MXN
Availability: InStock
Author: Airoldi, Edoardo M. (Editor)/ Blei, David (Editor)/ Erosheva, Elena A. (Editor)/ Fienberg, Stephen E. (Editor)
ISBN: 9780367330842

portada Handbook of Mixed Membership Models and Their Applications (en Inglés)

Handbook of Mixed Membership Models and Their Applications (en Inglés)

Formato

Libro Físico

Editorial

Crc Pr Inc

Autor

Airoldi, Edoardo M. (Editor)/ Blei, David (Editor)/ Erosheva, Elena A. (Editor)/ Fienberg, Stephen E. (Editor)

Idioma

Inglés

Encuadernación

Tapa Blanda

ISBN13

9780367330842

Handbook of Mixed Membership Models and Their Applications (en Inglés)

Airoldi, Edoardo M. (Editor)/ Blei, David (Editor)/ Erosheva, Elena A. (Editor)/ Fienberg, Stephen E. (Editor) (Autor) · Crc Pr Inc · Tapa Blanda

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Reseña del libro "Handbook of Mixed Membership Models and Their Applications (en Inglés)"

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.