Search for dissertations about: "Matematisk Statistik"
Showing result 6 - 10 of 376 swedish dissertations containing the words Matematisk Statistik.
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6. 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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7. Data driven modeling in the presence of time series structure: : Improved bounds and effective algorithms
Abstract : This thesis consists of five appended papers devoted to modeling tasks where the desired models are learned from data sets with an underlying time series structure. We develop a statistical methodology for providing efficient estimators and analyzing their non-asymptotic behavior. READ MORE
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8. Probabilistic machine learning methods for automated radiation therapy treatment planning
Abstract : In this thesis, different parts of an automated process for radiation therapy treatment planning are investigated from a mathematical and computational perspective. Whereas traditional inverse planning is labor-intensive, often comprising several reiterations between treatment planner and physician before a plan can be approved, much of recent research have been aimed at using a data-driven approach by learning from historically delivered plans. READ MORE
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9. Approximations of Bayes Classifiers for Statistical Learning of Clusters
Abstract : It is rarely possible to use an optimal classifier. Often the classifier used for a specific problem is an approximation of the optimal classifier. Methods are presented for evaluating the performance of an approximation in the model class of Bayesian Networks. READ MORE
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10. On approximations and computations in probabilistic classification and in learning of graphical models
Abstract : Model based probabilistic classification is heavily used in data mining and machine learning. For computational learning these models may need approximation steps however. READ MORE