Estimating Parameters of a Directed Weighted Graph Model with Beta-Distributed Edge-Weights


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Abstract

We introduce a directed, weighted random graph model, where the edge-weights are independent and beta distributed with parameters depending on their endpoints. We will show that the row- and column-sums of the transformed edge-weight matrix are sufficient statistics for the parameters, and use the theory of exponential families to prove that the ML estimate of the parameters exists and is unique. Then an algorithm to find this estimate is introduced together with convergence proof that uses properties of the digamma function. Simulation results and applications are also presented.

About the authors

M. Bolla

Institute of Mathematics, Budapest University of Technology and Economics

Author for correspondence.
Email: marib@math.bme.hu
Hungary, Budapest

J. Mala

Institute of Mathematics, Budapest University of Technology and Economics; Institute of Mathematics, ELTE Eötvös Loránd University

Email: marib@math.bme.hu
Hungary, Budapest; Budapest

A. Elbanna

Institute of Mathematics, Budapest University of Technology and Economics; Faculty of Science, Mathenmatics Department, Tanta University

Email: marib@math.bme.hu
Hungary, Budapest; Tanta

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