Estimating Information-Theoretic Quantities from Data
Deadline for manuscript submissions: closed (31 January 2013) | Viewed by 98193
Information-theoretic methods have become a workhorse of interdisciplinary research in computational molecular biology, computational neuroscience, ecology, social communications, and other fields. They are used for inference of interaction networks (such as protein networks or neural wiring diagrams), for understanding communication within these networks, and for building dynamical models of input-output behavior in them. They are further used to quantify diversity and stability of ecological niches, to characterize social interactions among individuals, and to develop assays for diseases and other abnormalities. One of the key problems slowing wider acceptance of these methods is the difficulty of reliable estimation of entropy and other information-theoretic quantities from empirical data. The field has made a remarkable progress in this direction in the recent years, and this Special Issue will explore this progress. We welcome contributions that explore methodological and algorithmic advances, applications to specialized data-driven research problems in the various fields of science, and theoretical investigations that explore the limits of our ability to solve the formidable problem of entropy and information estimation.
Dr. Ilya Nemenman
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- entropy estimation
- information estimation
- maximum entropy models
- information statistics
- mutual information
- kernel methods
- string matching
- Bayesian methods
- Shannon entropy
- Renyi entropy
- small data sets