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Nomvelo Sibisi

Publications and source records attributed to Nomvelo Sibisi.

3 recordsLinked to original sources

Infinitely Divisible Distributions and Commutative Diagrams

We study infinitely divisible (ID) distributions on the nonnegative half-line $\mathbb{R}_+$. The Lévy-Khintchine representation of such distributions is well-known. Our primary contribution is to cast the probabilistic objects and the relations amongst them in a unified visual form that we refer to as the Lévy-Khintchine commutative diagram (LKCD). While it is introduced as a representational tool, the LKCD facilitates the exploration of new ID distributions and may thus also be looked upon, at least in part, as a discovery tool. The basic object of the study is the gamma distribution. Closely allied to this is the $α$-stable distribution on $\mathbb{R}_+$ for $0<α<1$, which we regard as arising from the gamma distribution rather than as a separate object. It is characterised by its Laplace transform $\exp(-s^α)$ for $0<α<1$. It is indeed often characterised as an instance of a class of ID distributions known as generalised gamma convolutions (GGCs). We make use of convolutions and mixtures of gamma and stable densities to generate densities of other GGC distributions, with particular cases involving Bessel, confluent hypergeometric, Mittag-Leffler and parabolic cylinder functions. We present all instances as LKCD representations.

math.PR

A Cluster Model for Growth of Random Trees

We first consider the growth of trees by probabilistic attachment of new vertices to leaves. This leads to a growth model based on vertex clusters and probabilities assigned to clusters. This model turns out to be readily applicable to attachment at any depth of the tree, hence the paper evolves to a general study of tree growth by cluster-based attachment. Drawing inspiration from the concept of intrinsic vertex fitness due to Bianconi and Barabási, we introduce vertex mass as an additive intrinsic vertex attribute. Unlike Bianconi and Barabási who used fitness as a vertex degree multiplier in the context of growth by preferential attachment, we treat vertex mass as a fundamental probabilistic construct whose additivity plays a primary role. Notably, independent mass distributions induce a distribution on the sum of such masses through Laplace convolution. In this way, clusters of vertices inherit their mass distributions from vertices within the cluster. Our main contribution is a novel theorem for the joint distribution of cluster masses, conditioned on their respective distributions. As described by Ferguson and Kingman in the context of distributions on general measures, the choice of gamma conditioning distributions leads to the Dirichlet distribution. Beyond gamma conditioning distributions, our theorem allows other choices, such as the fat-tailed stable distributions with infinite mean. We discuss Lévy conditioning distributions as a gamma alternative, the Lévy distribution being a notable instance of the stable family. We conclude with a theorem giving the analytic marginals of the normalised distribution conditioned on the Lévy distribution.

math.PR

Growth of Random Trees by Leaf Attachment

We study the growth of a time-ordered rooted tree by probabilistic attachment of new vertices to leaves. We construct a likelihood function of the leaves based on the connectivity of the tree. We take such connectivity to be induced by the merging of directed ordered paths from leaves to the root. Combining the likelihood with an assigned prior distribution leads to a posterior leaf distribution from which we sample attachment points for new vertices. We present computational examples of such Bayesian tree growth. Although the discussion is generic, the initial motivation for the paper is the concept of a distributed ledger, which may be regarded as a time-ordered random tree that grows by probabilistic leaf attachment.

cs.DS