Search for dissertations about: "Bayesian recursive"
Showing result 6 - 10 of 19 swedish dissertations containing the words Bayesian recursive.
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6. Identification using Convexification and Recursion
Abstract : System identification studies how to construct mathematical models for dynamical systems from the input and output data, which finds applications in many scenarios, such as predicting future output of the system or building model based controllers for regulating the output the system.Among many other methods, convex optimization is becoming an increasingly useful tool for solving system identification problems. READ MORE
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7. Spatio-Temporal Estimation for Mixture Models and Gaussian Markov Random Fields - Applications to Video Analysis and Environmental Modelling
Abstract : In this thesis computationally intensive methods are used to estimate models and to make inference for large, spatio-temporal data sets. The thesis is divided into two parts: the first two papers are concerned with video analysis, while the last three papers model and investigate environmental data from the Sahel area in northern Africa. READ MORE
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8. Particle filtering for positioning and tracking applications
Abstract : A Bayesian approach to positioning and tracking applications naturally leads to a recursive estimation formulation. The recently invented particle filter provides a numerical solution to the non-tractable recursive Bayesian estimation problem. As an alternative, traditional methods such as the extended Kalman filter. READ MORE
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9. Parallelization of stochastic estimation algorithms on multicore computational platforms
Abstract : The main part of this licentiate thesis concerns parallelization of recursive estimation methods, both linear and nonlinear. Recursive estimation deals with the problem of extracting information about parameters or states of a dynamical system, given noisy measurements of the system output and plays a central role in many applications of signal processing, system identification, and automatic control. READ MORE
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10. Optimal Segmentation of Linear Regression Parameters
Abstract : The problem of detecting multiple changes in the dynamical properties of a measured signal, which we call segmentation, is studied. A Bayesian model-based approach is used. The signal is supposed to be described by a linear regression. The posterior distribution for the change instants is first derived for a quite general signal model. READ MORE