Search for dissertations about: "large deviations"
Showing result 1 - 5 of 169 swedish dissertations containing the words large deviations.
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1. On large deviations and design of efficient importance sampling algorithms
Abstract : This thesis consists of four papers, presented in Chapters 2-5, on the topics large deviations and stochastic simulation, particularly importance sampling. The four papers make theoretical contributions to the development of a new approach for analyzing efficiency of importance sampling algorithms by means of large deviation theory, and to the design of efficient algorithms using the subsolution approach developed by Dupuis and Wang (2007). READ MORE
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2. Large deviation techniques applied to three questions of when
Abstract : Large deviation techniques are used to solve three problems; when is a distant convex barrier passed, when to accept a sequence of gambles and when is the time of ruin. This work is the collection of four papers. READ MORE
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3. Topics on Large Deviations in Artificial Intelligence
Abstract : Artificial intelligence has become one of the most important fields of study during the last decade. Applications include medical sciences, autonomous vehicles, finance and everyday life. Therefore the analysis of convergence and stability of these algorithms is of utmost importance. READ MORE
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4. Large deviations of condition numbers of random matrices
Abstract : Random matrix theory has found many applications in various fields such as physics, statistics, number theory and so on. One important approach of studying random matrices is based on their spectral properties. In this thesis, we investigate the limiting behaviors of condition numbers of suitable random matrices in terms of large deviations. READ MORE
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5. Markov Chain Monte Carlo Methods and Applications in Neuroscience
Abstract : An important task in brain modeling is that of estimating model parameters and quantifying their uncertainty. In this thesis we tackle this problem from a Bayesian perspective: we use experimental data to update the prior information about model parameters, in order to obtain their posterior distribution. READ MORE