Information Theory (Mathematics)

Complexity and Information: Measuring Emergence, Self-organization, and Homeostasis at Multiple Scales

by Nelson Fernández

Co-authored with Carlos Gershenson. Submitted to Complexity.

Concepts used in the scientific study of complex systems have become so widespread that their use and abuse has led to... more Concepts used in the scientific study of complex systems have become so widespread that their use and abuse has led to ambiguity and confusion in their meaning. In this paper we use information theory to provide abstract and concise measures of complexity, emergence, self-organization, and homeostasis. The purpose is to clarify the meaning of these concepts with the aid of the proposed formal measures. In a simplified version of the measures (focussing on the information produced by a system), emergence becomes the opposite of self-organization, while complexity represents their balance. We use computational experiments on random Boolean networks and elementary cellular automata to illustrate our measures at multiple scales.

Some Qualitative Inferences on Stochastic Matrices via Information Theory

by Emmanuel Gonzalez

Published in the Proceedings of the 5th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), The Institute of Electrical and Electronics Engineers Inc. (IEEE) - Philippines Section, March 10-13, 2011 at the Traders Hotel in Manila, Philippines.
Co-authored with Aliento V. Estalilla (Ret. De La Salle University Manila) and Tirso A. Ronquillo (Batangas State Uniersity)

Information-theoretic bound on the energy cost of stochastic simulation

by Mile Gu

Equality conditions for internal entropies of certain classical and quantum models

by Peter Gmeiner

Mathematical models use information from past observations to generate predictions about the future. If two models... more Mathematical models use information from past observations to generate predictions about the future. If two models make identical predictions the one that needs less information from the past to do this is preferred. It is already known that certain classical models (certain Hidden Markov Models called \epsilon-machines which are often optimal classical models) are not in general the preferred ones. We extend this result and show that even optimal classical models (models with minimal internal entropy) in general are not the best possible models (called ideal models). Instead of optimal classical models we can construct quantum models which are significantly better but not yet the best possible ones (i.e. they have a strictly smaller internal entropy). In this paper we show conditions when the internal entropies between classical models and specific quantum models coincide. Furthermore it turns out that this situation appears very rarely. An example shows that our results hold only for the specific quantum model construction and in general not for alternative constructions. Furthermore another example shows that classical models with minimal internal entropy need not to be related to quantum models with minimal internal entropy.

Information Analysis of Human Splice Site Mutations

by Thomas Schneider

Toward a theory of node collaboration in wireless networks

by Kavé Salamatian

An information theory for erasure channels

by Kavé Salamatian

On the achievability of cut-set bound for a class of erasure relay channels

by Kavé Salamatian

On the capacity of multiple input erasure relay channels

by Kavé Salamatian

on the achievability of cut-set bound for a class of erasure relay channels: The non-degraded case

by Kavé Salamatian

On the capacity of erasure relay channel: Multi-relay case

by Kavé Salamatian