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|Title:||The data processing inequality and stochastic resonance|
|Citation:||Noise in complex systems and stochastic dynamics : 2-4 June 2003, Santa Fe, New Mexico, USA / Lutz Schimansky-Geier, Derek Abbott, Alexander Neiman, Christian Van den Broeck (eds.), pp. 249-260|
|Publisher Place:||Washington, USA|
|Series/Report no.:||Proceedings of SPIE--the International Society for Optical Engineering ; 5114|
|Conference Name:||Noise in Complex Systems and Stochastic Dynamics (2003 : Santa Fe, New Mexico, USA)|
|Mark D. McDonnell, Nigel G. Stocks, Charles E. M. Pearce, and Derek Abbott|
|Abstract:||The data processing inequality of information theory states that given random variables X, Y and Z which form a Markov chain in the order X-->Y-->Z, then the mutual information between X and Y is greater than or equal to the mutual information between X and Z. That is I(X) >= I(X;Z) . In practice, this means that no more information can be obtained out of a set of data then was there to begin with, or in other words, there is a bound on how much can be accomplished with signal processing. However, in the field of stochastic resonance, it has been reported that a signal to noise ratio gain can occur in some nonlinear systems due to the addition of noise. Such an observation appears to contradict the data processing inequality. In this paper, we investigate this question by using an example model system.|
|Description:||© 2003 COPYRIGHT SPIE--The International Society for Optical Engineering|
|Appears in Collections:||Applied Mathematics publications|
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