Accuracy of approximation for discrete distributions

The paper is a contribution to the problem of estimating the deviation of two discrete probability distributions in terms of the supremum distance between their generating functions over the interval [ 0,1 ]. Deviation can be measured by the difference of the k th terms or by total variation distance. Our new bounds have better order of magnitude than those proved previously, and they are even sharp in certain cases. © 2016 Tamás F. Móri

Further properties of a random graph with duplications and deletions

We deal with a random graph model where at each step, a vertex is chosen uniformly at random, and it is either duplicated or its edges are deleted. Duplication has a given probability. We analyze the limit distribution of the degree of a fixed vertex and derive a.s. asymptotic bounds for the maximal degree. The model shows a phase transition phenomenon with respect to the probabilities of duplication and deletion. © 2016 Taylor & Franci

Further properties of a random graph with duplications and deletions

We deal with a random graph model where at each step, a vertex is chosen uniformly at random, and it is either duplicated or its edges are deleted. Duplication has a given probability. We analyze the limit distribution of the degree of a fixed vertex and derive a.s. asymptotic bounds for the maximal degree. The model shows a phase transition phenomenon with respect to the probabilities of duplication and deletion. © 2016 Taylor & Franci

ASYMPTOTIC PROPERTIES OF A RANDOM GRAPH WITH DUPLICATIONS

We deal with a random graph model evolving in discrete time steps by duplicating and deleting the edges of randomly chosen vertices. We prove the existence of an almost surely asymptotic degree distribution, with stretched exponential decay; more precisely, the proportion of vertices of degree d tends to some positive number c(d) > 0 almost surely as the number of steps goes to infinity, and c(d) similar to (e pi)(1/2)d(1/4)e(-2)root d holds as d -> infinity

Testing goodness-of-fit of random graph models

Random graphs are matrices with independent 0, 1 elements with probabilities determined by a small number of parameters. One of the oldest model is the Rasch model where the odds are ratios of positive numbers scaling the rows and columns. Later Persi Diaconis with his coworkers rediscovered the model for symmetric matrices and called the model beta. Here we give goodnes-of-fit tests for the model and extend the model to a version of the block model introduced by Holland, Laskey, and Leinhard