Search for dissertations about: "Kalman markov"
Showing result 1 - 5 of 9 swedish dissertations containing the words Kalman markov.
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1. Probabilistic Sequence Models with Speech and Language Applications
Abstract : 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. READ MORE
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2. Multiple Model Filtering and Data Association with Application to Ground Target Tracking
Abstract : This thesis is concerned with two central parts of a tracking system, namely multiple-model filtering and data association. Multiple models are introduced to provide accurate filtering, whereas data association deals with the unknown origin of the received measurements. READ MORE
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3. GPS/IMU Integrated System for Land Vehicle Navigation based on MEMS
Abstract : The Global Positioning System (GPS) and an Inertial Navigation System (INS)are two basic navigation systems. Due to their complementary characters in manyaspects, a GPS/INS integrated navigation system has been a hot research topic inthe recent decade. Both advantages and disadvantages of each individual systemare analyzed. READ MORE
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4. On inference in partially observed Markov models using sequential Monte Carlo methods
Abstract : This thesis concerns estimation in partially observed continuous and discrete time Markov models and focus on both inference about the conditional distribution of the unobserved process as well as parameter inference for the dynamics of the unobserved process. Paper A concerns calibration of advanced stock price models, in particular the Bates and NIG-CIR models, using options data observed through bid-ask spreads. READ MORE
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5. 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