Large Deviations, Basic Information Theorem for Fitness Preferential Attachment Random Networks

K. Doku-Amponsah, F. O. Mettle, T. Ansah-Narh

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.

Full Text: PDF DOI: 10.5539/ijsp.v3n2p101

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International Journal of Statistics and Probability   ISSN 1927-7032(Print)   ISSN 1927-7040(Online)

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