Search for dissertations about: "Expectation-Maximization Algorithm"
Showing result 11 - 15 of 44 swedish dissertations containing the words Expectation-Maximization Algorithm.
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11. On Bounds and Asymptotics of Sequential Monte Carlo Methods for Filtering, Smoothing, and Maximum Likelihood Estimation in State Space Models
Abstract : This thesis is based on four papers (A-D) treating filtering, smoothing, and maximum likelihood (ML) estimation in general state space models using stochastic particle filters (also referred to as sequential Monte Carlo (SMC) methods). The aim of Paper A is to study the bias of Monte Carlo integration estimates produced by the so-called bootstrap particle filter. READ MORE
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12. On particle-based online smoothing and parameter inference in general hidden Markov models
Abstract : This thesis consists of two papers studying online inference in general hidden Markov models using sequential Monte Carlo methods.The first paper present an novel algorithm, the particle-based, rapid incremental smoother (PaRIS), aimed at efficiently perform online approximation of smoothed expectations of additive state functionals in general hidden Markov models. READ MORE
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13. On particle-based online smoothing and parameter inference in general state-space models
Abstract : This thesis consists of 4 papers, presented in Paper A-D, on particle- based online smoothing and parameter inference in general state-space hidden Markov models.In Paper A a novel algorithm, the particle-based, rapid incremental smoother (PaRIS), aimed at efficiently performing online approxima- tion of smoothed expectations of additive state functionals in general hidden Markov models, is presented. READ MORE
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14. Available-Bandwidth Estimation in Packet-Switched Communication Networks
Abstract : This thesis presents novel methods that are able to perform real-time estimation of the available bandwidth of a network path. In networks such as the Internet, knowledge of bandwidth characteristics is of great significance in, e.g., network monitoring, admission control, and audio/video streaming. READ MORE
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15. Novel likelihood-based inference techniques for sequential data with medical and biological applications
Abstract : The probabilistic approach is crucial in modern machine learning, as it provides transparency and quantification of uncertainty. This thesis is concerned with the probabilistic building blocks, i.e., probabilistic graphical models (PGM) followed by application of standard deterministic approximate inference, i. READ MORE
