Search for dissertations about: "random"
Showing result 1 - 5 of 1680 swedish dissertations containing the word random.
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1. Random tournaments and random circuits
Abstract : This thesis is devoted to two different topics in the area of probabilistic combinatorics: asymptotic behaviour of subgraph counts in a random tournament and random circuit decompositions of complete graphs.Let Tn be a random tournament on n vertices, chosen uniformly from all 2(n2) such tournaments, and let D be an arbitrary directed graph. READ MORE
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2. Random Multigraphs : Complexity Measures, Probability Models and Statistical Inference
Abstract : This thesis is concerned with multigraphs and their complexity which is defined and quantified by the distribution of edge multiplicities. Two random multigraph models are considered. The first model is random stub matching (RSM) where the edges are formed by randomly coupling pairs of stubs according to a fixed stub multiplicity sequence. READ MORE
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3. Dynamics in Random Boolean Networks
Abstract : There are many examples of complex networks in science. It can be genetic regulation in living cells, computers on the Internet, or social and economic networks. In this context, Boolean networks provide simplistic models that are relatively easy to handle using computer simulations and mathematical methods. READ MORE
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4. Random railways and cycles in random regular graphs
Abstract : In a cubic multigraph certain restrictions on the paths are made to define what is called a railway. Due to these restrictions a special kind of connectivity is defined. As the number of vertices tends to infinity, the asymptotic probability of obtaining an, in this sense, connected random cubic multigraph is shown to be 1/3. READ MORE
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5. Random geometric graphs and their applications in neuronal modelling
Abstract : Random graph theory is an important tool to study different problems arising from real world.In this thesis we study how to model connections between neurons (nodes) and synaptic connections (edges) in the brain using inhomogeneous random distance graph models. READ MORE