Estimating Random walk Centrality
| dc.contributor.author | Epasinghege Dona, Nirodha Mihirani | |
| dc.contributor.examiningcommittee | Muthukumarana, Saman (Statistics) Kirkland, S (Mathematics) | en_US |
| dc.contributor.supervisor | Johnson, Brad (Statistics) | en_US |
| dc.date.accessioned | 2019-08-19T18:40:41Z | |
| dc.date.available | 2019-08-19T18:40:41Z | |
| dc.date.issued | 2019 | en_US |
| dc.date.submitted | 2019-08-13T19:31:05Z | en |
| dc.degree.discipline | Statistics | en_US |
| dc.degree.level | Master of Science (M.Sc.) | en_US |
| dc.description.abstract | Centrality measures play an important role in determining the importance of vertices in networks. For strongly connected networks, the random walk centrality measures how easy it is to reach a given state from another randomly chosen state. This measure requires calculating a generalized group inverse for the transition matrix, which can be computationally difficult for large state spaces. It is known that the random walk centrality for a particular state can be written as a function of the first and second moments of the inter-arrival times for that state. In this study, using the realization of random walks, we estimate these moments by using a number of statistical methods, including Bayesian bootstrap and two Poisson mixture model approaches. Finally, we compare the resulting estimates of the random walk centrality measures to the true values. | en_US |
| dc.description.note | October 2019 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/34074 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Random Walk Centrality | en_US |
| dc.subject | Bayesian bootstrap | en_US |
| dc.subject | Finite Poisson Mixtures | en_US |
| dc.subject | Bayesian Analysis | en_US |
| dc.title | Estimating Random walk Centrality | en_US |
| dc.type | master thesis | en_US |