Impressive contributions in this volume address many aspects of information theory concepts, measures, and applications. It is a multidisciplinary tour de force, covering foundations, inference, and applications to finance, computing, behavioral models, and much more.
Min Chen is the Professor of Scientific Visualization at Oxford University and a fellow of Pembroke College. He has co-authored over 200 publications, including his recent contributions in areas such as theory of visualization, video visualization, visual analytics, and perception and cognition in visualization.
J. Michael Dunn is Oscar Ewing Professor Emeritus of Philosophy, Professor Emeritus of Informatics and Computer Science, at Indiana University, where he spent most of his career and was founding dean of the School of Informatics. He is an affiliate member of the Info-Metrics Institute at the American University. His research has focused on information based logics.
Amos Golan is Professor of Economics and Director of the Info-Metrics Institute at American University. He is also an External Professor at the Santa Fe Institute and a Senior Associate at Pembroke College, Oxford. A leader in info-metrics, he is the author of Foundations of Info-Metrics: Information, Inference, and Incomplete Information.
Aman Ullah is Distinguished Professor of Economics at the University of California, Riverside. The author of 10 books and more than 160 published articles, Professor Ullah has helped shape the field of econometrics and has pioneered the development and application of non-parametric and semi-parametric methods.