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 20 of 20 for “"dynamic Bayesian networks"”.
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Computational intelligent systems : evolving dynamic Bayesian networks
… probabilistic modelling, called evolving dynamic Bayesian networks (EDBN), is proposed and demonstrated to make technology easier so as to accommodate both experts and non-experts, such as industrial practitioners, decision-makers, researchers, etc. Dynamic Bayesian Networks (DBNs) are …
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Dynamic Bayesian networks for the classification of spinning discs
… of spinning discs. The system combines linear dynamics describing rotation with a nonlinear observation model determined by the disc pattern, which is parameterized by angle. A consequence of the nonlinear observation model is that the posterior state distribution of angle and spin-rate is …
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Dynamic Bayesian Networks for Information Fusion With Applications to Human-Computer Interfaces
… a novel probabilistic approach based on dynamic Bayesian networks (DBNs). As a generalization of the successful hidden Markov models, DBNs are a natural basis for the general temporal action interpretation task. The problem of interpretation of single or multiple interacting modalities …
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A temporal prognostic model based on dynamic Bayesian networks: mining medical insurance data
… groups who have similar prognostic paths. Dynamic Bayesian networks theoretically provide a very expressive and flexible model to solve temporal problems in medicine. However, this involves various challenges due both to the nature of the clinical domain, and the nature of the DBN modelling …
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Safety of Flight Prediction for Small Unmanned Aerial Vehicles Using Dynamic Bayesian Networks
This thesis compares three variations of the Bayesian network as an aid for decision-making using uncertain information. After reviewing the basic theory underlying probabilistic graphical models and Bayesian estimation, the thesis presents a user-defined static Bayesian network, a static Bayesian …
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Bayesian networks for spatio-temporal integrated catchment assessment
… catchment water resources assessment using Bayesian Networks was developed. A custom made software application that combines Bayesian Networks with GIS was used to facilitate data pre-processing and spatial modelling. Dynamic Bayesian Networks were implemented in the software for time-series …
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Aspects of inference for the Influence Model and related graphical models
… the primary motivation of describing network dynamics in power systems, has proved to be very useful in a variety of contexts. It consists of a directed graph of interacting sites whose Markov state transition probabilities depend on their present state and that of their neighbors. The major …
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Multiple Uses of Frequent Episodes in Temporal Process Modeling
… for modeling temporal processes such as motifs, dynamic Bayesian networks and partial orders, but the direct inference of such models from data has been computationally intensive or even intractable. In this work, we propose the mining of frequent episodes as a bridge to inferring more formal …
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Feature-based pronunciation modeling for automatic speech recognition
… pronunciation models represented as dynamic Bayesian networks (DBNs).
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Bayesian nonparametric approaches for reinforcement learning in partially observable domains
… representations of stochastic systems using Bayesian nonparametric statistics. Bayesian nonparametric methods allow the sophistication of a representation to scale gracefully with the complexity in the data. We show how the representations learned using Bayesian nonparametric methods result …
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Extending expectation propagation for graphical models
… needed to handle complex models, such as hybrid Bayesian networks. This thesis proposes extensions of expectation propagation, a powerful generalization of loopy belief propagation, to develop efficient Bayesian inference and learning algorithms for graphical models. The first two chapters of the …
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On the design and implementation of decision-theoretic, interactive, and vision-driven mobile robots
… tackling the problem of multi-step actions using Dynamic Bayesian Networks. In addition, we describe a state-of-the-art simultaneous localization and mapping algorithm for robots equipped with stereo vision. We first present the Monte-Carlo algorithm sigmaMCL for robot localization in 3D using …
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Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases
… uncertainty suits well to analysis of disease dynamics. The stochastic nature of disease progression is modeled by applying the principles of Bayesian learning. Bayesian learning predicts the disease progression, including prevalence and incidence, for a geographic region and demographic …
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An assessment of the onset of summer rainy season in Southern Africa - case study of Botswana
… Emergent Situation Awareness (ESA) for dynamic Bayesian networks (DBN) was used to analyze this data. The ESA for DBN models temporal dependencies among the weather parameters and climate indices using Direct Acyclic Graphs (DAG). This innovative DBN technology, ESA, reveals more …
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Design and implementation of advanced Bayesian networks with comparative probability
… the frequency interpretation of probability, dynamic Bayesian networks and the expected utility theory. It enables engineers to write self-learning algorithms that use example of behaviours to model situations, evaluate and make decisions, diagnose problems, and/or find the most probable …
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Deception detection in dialogues
… background knowledge in the form of a relational dynamic Bayesian network structure.
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Evolving and Proactive Risk Modelling in Underground Working Environments
… yet they are inherently hazardous due to their dynamic and unpredictable nature. Rapid changes, including methane accumulation and environmental fluctuations, threaten worker safety, operational continuity, and infrastructure integrity. Traditional risk assessment methods, which rely on static …
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Performance-Based Coastal Engineering Framework
The changing dynamics of coastal regions and climate pose severe challenges to coastal communities around the world. Such challenges are exacerbated by an increase in population over time coupled with the aging of existing infrastructure, rise in property value, and the expected shifts in the …
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Probabilistic Approximation and Analysis Techniques for Bio-Pathway Models
Quantitative modeling of bio-pathway dynamics is crucial to the system-level understanding of cellular functions and behavior. Currently, a common method of representing bio-pathways is through a system of ordinary differential equations (ODEs). However, calibrating and analyzing large ODE-based …
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Towards practical automated human action recognition
… on discrete latent variables, such as dynamic Bayesian networks (DBNs) and switching models . As another contribution, we propose a simple and computationally lightweight feature set, named sectorial extreme points, which requires only 1.6 ms per frame for extraction on a reference PC. …