Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||Towards an information-theoretic model of the Allison mixture stochastic process|
|Citation:||Journal of Statistical Mechanics: Theory and Experiment, 2016; 2016(5):054041|
|Lachlan J Gunn, François Chapeau-Blondeau, Andrew Allison and Derek Abbott|
|Abstract:||The Allison mixture is a random process formed by stochastically switching between two random and uncorrelated input processes. Unintuitively, these samples—independent prior to being drawn—can acquire dependence as a result of the sampling process. It has previously been shown that correlation can occur subject to certain conditions, however in general dependence does not imply correlation. In this paper we provide an initial information-theoretic analysis of the Allison mixture, and derive the autoinformation function of its sampling process as the first step towards a fuller information-theoretic analysis of its output.|
|Rights:||© 2016 IOP Publishing Ltd and SISSA Medialab srl|
|Appears in Collections:||Mathematical Sciences publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.