Probabilistic Inference and Statistical Methods in Network Analysis

This edited book, Probabilistic Inference and Statistical Methods in Network Analysis, is a collection of contemporary open access articles which highlight the development of computational methods for constructing social and biological networks; detecting the topological structure of a network and identifying important nodes within. This book features two classes of computational methods currently used in network analysis: (a) model-free methods based on statistical and information theory measures such as centrality, correlation, cross-correlation, and partial-correlation, mutual information, joint entropy, and transfer entropy; and (b) generative model-based methods. The intended audience of this edited book will mainly consist of researchers and graduate students in the Natural and Computer Sciences. The book is also of particular interest to scientists and engineers in areas such as machine learning, data mining, information theory computational neuroscience, and biological systems. It is suitable for readers with basic knowledge of statistical inference, differential equations, calculus, algebra, graph theory scientific modelling and computer simulation. Book jacket.