DSpace Collection:
http://hdl.handle.net/2440/1087
2016-09-30T18:34:43ZInfluences of Allee effects in the spreading of malignant tumours
http://hdl.handle.net/2440/101450
Title: Influences of Allee effects in the spreading of malignant tumours
Author: Sewalt, L.; Harley, K.; van Heijster, P.; Balasuriya, S.
Abstract: Abstract not available2016-01-01T00:00:00ZOn binomial observations of continuous-time Markovian population models
http://hdl.handle.net/2440/101388
Title: On binomial observations of continuous-time Markovian population models
Author: Bean, N.G.; Elliott, R.; Eshragh, A.; Ross, J.V.
Abstract: In this paper we consider a class of stochastic processes based on binomial observations of continuous-time, Markovian population models. We derive the conditional probability mass function of the next binomial observation given a set of binomial observations. For this purpose, we first find the conditional probability mass function of the underlying continuous-time Markovian population model, given a set of binomial observations, by exploiting a conditional Bayes, theorem from filtering, and then use the law of total probability to find the former. This result paves the way for further study of the stochastic process introduced by the binomial observations. We utilize our results to show that binomial observations of the simple birth process are non-Markovian.2015-01-01T00:00:00ZSpatiotemporal traffic matrix synthesis
http://hdl.handle.net/2440/101357
Title: Spatiotemporal traffic matrix synthesis
Author: Tune, P.; Roughan, M.
Abstract: Traffic matrices describe the volume of traffic between a set of sources and destinations within a network. These matrices are used in a variety of tasks in network planning and traffic engineering, such as the design of network topologies. Traffic matrices naturally possess complex spatiotemporal characteristics, but their proprietary nature means that little data about them is available publicly, and this situation is unlikely to change. Our goal is to develop techniques to synthesize traffic matrices for researchers who wish to test new network applications or protocols. The paucity of available data, and the desire to build a general framework for synthesis that could work in various settings requires a new look at this problem. We show how the principle of maximum entropy can be used to generate a wide variety of traffic matrices constrained by the needs of a particular task, and the available information, but otherwise avoiding hidden assumptions about the data. We demonstrate how the framework encompasses existing models and measurements, and we apply it in a simple case study to illustrate the value.2015-01-01T00:00:00ZEffective wave propagation along a rough thin-elastic beam
http://hdl.handle.net/2440/101269
Title: Effective wave propagation along a rough thin-elastic beam
Author: Rupprecht, S.; Bennetts, L.G.; Peter, M.A.
Abstract: Abstract not available2016-01-01T00:00:00Z