Search for dissertations about: "Bayes"
Showing result 11 - 15 of 57 swedish dissertations containing the word Bayes.
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11. Module identification in dynamic networks: parametric and empirical Bayes methods
Abstract : The purpose of system identification is to construct mathematical models of dynamical system from experimental data. With the current trend of dynamical systems encountered in engineering growing ever more complex, an important task is to efficiently build models of these systems. READ MORE
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12. Bridges with Random Length and Pinning Point for Modelling the Financial Information
Abstract : The impact of the information concerning an event of interest occurring at a future random time is the main topic of this work. The event can massively influence financial markets and the problem of modelling the information on the time at which it occurs is of crucial importance in financial modelling. READ MORE
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13. Towards practical and provable domain adaptation
Abstract : One of the most central questions in statistical modeling is how well a model will generalize. Absent strong assumptions we find that this question is difficult to answer in a meaningful way. In this work we seek to increase our understanding of the domain adaptation setting through two different lenses. READ MORE
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14. The role of forensic epidemiology in evidence-based forensic medical practice
Abstract : Objectives This thesis is based on 4 papers that were all written with the same intent, which was to describe and demonstrate how epidemiologic concepts and data can serve as a basis for improved validity of probabilistic conclusions in forensic medicine (FM). Conclusions based on probability are common in FM, and the validity of probabilistic conclusions is dependant on their foundation, which is often no more than personal experience. READ MORE
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15. Guaranteeing Generalization via Measures of Information
Abstract : During the past decade, machine learning techniques have achieved impressive results in a number of domains. Many of the success stories have made use of deep neural networks, a class of functions that boasts high complexity. Classical results that mathematically guarantee that a learning algorithm generalizes, i.e. READ MORE