Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 10 of 10 for “"Sequential Monte Carlo Methods"”.
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Sequential Monte Carlo Methods with Applications to Positioning and Tracking in Wireless Networks
… state-space models together with applications of Sequential Monte Carlo (also called particle filtering) methods to the positioning in wireless networks. The aim of the first paper is to study the performance of particle filtering techniques in mobile positioning using signal strength …
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Localization and separation of concurrent talkers based on principles of auditory scene analysis and multi-dimensional statistical methods
… it. To implement these, Bayesian estimation, sequential Monte Carlo methods, and statistical evaluation of speech databases are used. Three on-line algorithms are developed and tested, which run partly in real-time. They allow for a robust, efficient and exact sound localization even at low …
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Extending expectation propagation for graphical models
… achieves estimation accuracy comparable to sequential Monte Carlo methods, but with less than one-tenth computational cost. Second, it develops a new method that combines tree-structured EP approximations with the junction tree for inference on loopy graphs. This new method saves computation …
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Nonlinear Dynamics, Stochastic Methods, And Predictive Modelling For Infectious Disease: Application To Public Health And Epidemic Forecasting
… introduces flexible models and statistical methods designed to infer data-generating processes that vary temporally. The primary objective is to develop frameworks for efficient estimation and prediction of both univariate and multivariate time series data. The models considered are general …
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State space modelling of extreme values with particle filters
… distribution can change gradually over time. Sequential Monte Carlo methods known as particle filters provide an approach to inference for such models whereby observations are added to the fit sequentially. Though originally developed for on-line inference, particle filters, along with related …
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Formally justified and modular Bayesian inference for probabilistic programs
… inference is not tractable and approximate methods are used instead, posing a question of how the results of these algorithms relate to the exact results. Correctness of such approximate methods is usually argued somewhat less rigorously, without reference to a formal semantics. In this …
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New model-based methods for non-differentiable optimization
Model-based optimization methods are effective for solving optimization problems with little structure, such as convexity and differentiability. Such algorithms iteratively find candidate solutions by generating samples from a parameterized probabilistic model on the solution space, and update the …
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Monte Carlo integration in discrete undirected probabilistic models
… work in and contributions to the field of Monte Carlo sampling for undirected graphical models, a class of statistical model commonly used in machine learning, computer vision, and spatial statistics; the aim is to be able to use the methodology and resultant samples to estimate integrals …
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Using probability density functions to analyze the effect of external threats on the reliability of a South African power grid
… evaluation technique that is based on the sequential Monte Carlo simulation. The technique applies a time-dependent probabilistic modelling approach to network reliability parameters. The approach uses the Beta probability density functions to model stochastic network parameters while …
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Novel Hybrid Resampling Algorithms for Parallel/Distributed Particle Filters
<p>Particle filters, also known as sequential Monte Carlo (SMC) methods, use the Bayesian inference and the stochastic sampling technique to estimate the states of dynamic systems from given observations. Parallel/Distributed particle filters were introduced to improve the performance of sequential …