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.
This is the third edition of the textbook, Inferential Statistics: Hypothesis Testing and Decision‐making, first published in 1995. It has been appropriately renamed Inferential Data Analysis to...
In this study two strands of inferentialism are brought together: the philosophical doctrine of Brandom, according to which meanings are generally inferential roles, and the logical doctrine...
Kant's Inferentialism draws on a wide range of sources to present a reading of Kant's theory of mental representation as a direct response to the challenges issued by Hume in A Treatise of Human...