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Visar resultat 1 - 5 av 10 avhandlingar som matchar ovanstående sökkriterier.
1. Kalman Filters for Nonlinear Systems and Heavy-Tailed Noise
Sammanfattning : This thesis is on filtering in state space models. First, we examine approximate Kalman filters for nonlinear systems, where the optimal Bayesian filtering recursions cannot be solved exactly. These algorithms rely on the computation of certain expected values. LÄS MER
2. Probabilistic Sequence Models with Speech and Language Applications
Sammanfattning : Series data, sequences of measured values, are ubiquitous. Whenever observations are made along a path in space or time, a data sequence results. To comprehend nature and shape it to our will, or to make informed decisions based on what we know, we need methods to make sense of such data. LÄS MER
3. Sequential Monte Carlo Filters and Integrated Navigation
Sammanfattning : In this thesis we consider recursive Bayesian estimation in general, and sequential Monte Carlo filters in particular, applied to integrated navigation. Based on a large number of simulations of the model, the sequential Monte Carlo filter, also referred to as particle filter, provides an empirical estimate of the full posterior probability density of the system. LÄS MER
4. Advanced Kalman Filtering Approaches to Bayesian State Estimation
Sammanfattning : Bayesian state estimation is a flexible framework to address relevant problems at the heart of existing and upcoming technologies. Application examples are obstacle tracking for driverless cars and indoor navigation using smartphone sensor data. LÄS MER
5. Classical and Quantum Correlations in Microwave Frequency Combs
Sammanfattning : Denna avhandling undersöker korrelationer i frekvensdomänen, av både klassiskt och kvantmekaniskt ursprung, i icke-linjära mikrovågskretsar. Syftet är att utveckla en kompakt metod för att generera kvantmekaniska korrelationer mellan harmoniska oscillatorer (s.k. LÄS MER