Additional information
| Full Title | Inferential Network Analysis |
|---|---|
| Author(s) | Skyler J. Cranmer, Bruce A. Desmarais, Jason W. Morgan |
| Edition | |
| ISBN | 9781009028684, 9781107158122 |
| Publisher | Cambridge University Press |
| Format | PDF and EPUB |
Availability: In Stock
Inferential Network Analysis
SKU: 9781009028684
Original price was: $60.99.$24.99Current price is: $24.99.
Access Inferential Network Analysis Now. Discount up to 90%
Book Information
Description
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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Inferential Network Analysis
SKU: 9781009028400
Original price was: $60.99.$24.99Current price is: $24.99.
Access Inferential Network Analysis Now. Discount up to 90%
Book Information
Additional information
| Full Title | Inferential Network Analysis |
|---|---|
| Author(s) | Skyler J. Cranmer, Bruce A. Desmarais, Jason W. Morgan |
| Edition | |
| ISBN | 9781009028400, 9781107158122 |
| Publisher | Cambridge University Press |
| Format | PDF and EPUB |
Description
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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