Description

This unique textbook provides an introduction to statistical inference with network data. The authors present a self-contained derivation and mathematical formulation of methods, review examples, and real-world applications, as well as provide data and code in the R environment that can be customised. Inferential network analysis transcends fields, and examples from across the social sciences are discussed (from management to electoral politics), which can be adapted and applied to a panorama of research. From scholars to undergraduates, spanning the social, mathematical, computational and physical sciences, readers will be introduced to inferential network models and their extensions. The exponential random graph model and latent space network model are paid particular attention and, fundamentally, the reader is given the tools to independently conduct their own analyses.

Inferential Network Analysis

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£43.99

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Paperback / softback by Skyler J. Cranmer , Bruce A. Desmarais

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This unique textbook provides an introduction to statistical inference with network data. The authors present a self-contained derivation and mathematical... Read more

    Publisher: Cambridge University Press
    Publication Date: 19/11/2020
    ISBN13: 9781316610855, 978-1316610855
    ISBN10: 1316610853

    Number of Pages: 314

    Non Fiction , Dictionaries, Reference & Language

    Description

    This unique textbook provides an introduction to statistical inference with network data. The authors present a self-contained derivation and mathematical formulation of methods, review examples, and real-world applications, as well as provide data and code in the R environment that can be customised. Inferential network analysis transcends fields, and examples from across the social sciences are discussed (from management to electoral politics), which can be adapted and applied to a panorama of research. From scholars to undergraduates, spanning the social, mathematical, computational and physical sciences, readers will be introduced to inferential network models and their extensions. The exponential random graph model and latent space network model are paid particular attention and, fundamentally, the reader is given the tools to independently conduct their own analyses.

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