Large deviations, basic information theorem for fitness preferential attachment random networks

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dc.contributor.author Doku-Amponsah, K
dc.contributor.author Mettle, F.O
dc.contributor.author Ansah-Narh, T
dc.date.accessioned 2015-06-23T11:54:22Z
dc.date.accessioned 2017-10-14T12:21:09Z
dc.date.available 2015-06-23T11:54:22Z
dc.date.available 2017-10-14T12:21:09Z
dc.date.issued 2014
dc.identifier.uri http://197.255.68.203/handle/123456789/6247
dc.description.abstract For fitness preferential attachment random networks, we define the empirical degree and pair measure, which counts the number of vertices of a given degree and the number of edges with given fits, and the sample path empirical degree distribution. For the empirical degree and pair distribution for the fitness preferential attachment random networks, we find a large deviation upper bound. From this result we obtain a weak law of large numbers for the empirical degree and pair distribution, and the basic information theorem or an asymptotic equipartition property for fitness preferential attachment random networks. en_US
dc.publisher International journal of Statistics and Probability en_US
dc.subject Large deviation upper bound en_US
dc.subject relative entropy en_US
dc.subject random network en_US
dc.subject random tree en_US
dc.subject random coloured graph en_US
dc.subject typed graph en_US
dc.subject asymptotic equipartition property en_US
dc.title Large deviations, basic information theorem for fitness preferential attachment random networks en_US
dc.type Article en_US


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