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Statistical inference and random network simulation

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Lahtinen, Jani
dc.date.accessioned 2012-02-17T06:45:28Z
dc.date.available 2012-02-17T06:45:28Z
dc.date.issued 2005-01-28
dc.identifier.isbn 951-22-7490-6
dc.identifier.issn 1457-1404
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/2526
dc.description.abstract The scope of this dissertation is twofold, in the sense that it deals on one hand with statistical inference and on the other hand with random graphs. Due to inherent randomness in both areas the scope can also be seen as onefold, which is further united methodologically by the attempt to build models of random processes involved and by simulating their behaviour. The statistical part of the thesis follows the Bayesian theory of probability, and applies it to a fault diagnostic setting. This part also contains an exploration of metrics on probability distributions, in which the introduction of a new metric is one of the main contributions. This new metric is constructed from utilities of the samples instead of the more conventional entropy-based metrics. In Bayesian methods the simulation of samples from distributions is an integral part of the analysis. It also becomes the leading principle in the evaluation of the proposed metrics. This metric is shown to be useful in statistical inference in some cases where the probabilities are difficult to compute. The problem of uncomputable likelihoods is analysed also from the Bayesian perspective and two branches emerge: the kernel estimate and the indirect inference. In the analysis of random graphs the attention is on the small-world property, requiring that any two sites in the network are joined by only a short path with a relatively small average number of connections per site. Again one of the main tools in analysing complex graphs is by simulation of random dynamics on the graphs. The first dynamic property that is analysed is the spreading phenomenon. Spreading means the number of unique sites a random walker on the graphs goes through. This number is shown to have transition points relative to the small-world control parameter. Apart from the spreading phenomenon the thesis also studies the self-organised criticality properties through the so called sandpile model on the one dimensional small-world networks. In this setting of self-organised criticality there are interesting behaviours that are absent in the standard 1-dimensional sandpile model. Both the spreading and the sandpile model are analysed with two forms of disorder: quenched and annealed. The quenched case corresponds to a simulation setting on an ensemble of random graphs, whereas in the case of annealed disorder the simulation is performed on a regular graph but the dynamics also allow random moves to other sites. The annealed form allows simpler analytic tools to be used, but the quenched form corresponds more closely to natural systems. Even though these forms of disorder are different it is shown that the annealed systems can be made to behave in a qualitatively similar fashion as the quenched case. en
dc.format.extent 86
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Helsinki University of Technology en
dc.publisher Teknillinen korkeakoulu fi
dc.relation.ispartofseries Helsinki University of Technology Laboratory of Computational Engineering publications. Report B en
dc.relation.ispartofseries 44 en
dc.subject.other Electrical engineering en
dc.title Statistical inference and random network simulation en
dc.type G4 Monografiaväitöskirja fi
dc.description.version reviewed en
dc.contributor.department Department of Electrical and Communications Engineering en
dc.contributor.department Sähkö- ja tietoliikennetekniikan osasto fi
dc.subject.keyword Bayesian inference en
dc.subject.keyword indirect inference en
dc.subject.keyword metrics of distributions en
dc.subject.keyword random graphs en
dc.subject.keyword self-organised criticality en
dc.subject.keyword Bayesilainen päättely fi
dc.subject.keyword epäsuora päättely fi
dc.subject.keyword metriikka fi
dc.subject.keyword satunnaisverkot fi
dc.subject.keyword itse-organisoituva kriittisyys fi
dc.identifier.urn urn:nbn:fi:tkk-004742
dc.type.dcmitype text en
dc.type.ontasot Väitöskirja (monografia) fi
dc.type.ontasot Doctoral dissertation (monograph) en
dc.contributor.lab Laboratory of Computational Engineering en
dc.contributor.lab Laskennallisen tekniikan laboratorio fi
local.aalto.digifolder Aalto_68363
local.aalto.digiauth ask

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