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 159 for “"Bayesian networks"”.
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Applied Bayesian Networks
<p>A Bayesian Network is a stochastic graphical model that can be used to maintain and propagate conditional probability tables among its nodes. Here, we use a Bayesian Network to model results from a numerical riverine model. We develop an discretization optimization algorithm that improves …
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Link strength in Bayesian networks
… (CS) between two nodes of a propositional Bayesian network (BN). Connection strength is a generalization of node independence, from a binary property to a graded measure. The connection strength from node A to node B is a measure of the maximum amount that the belief in B will change when …
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Bayesian networks for cardiovascular monitoring
… amount of time. In this thesis, I explore Bayesian Networks as a way to integrate patient data into a probabilistic model. I present a small Bayesian Network model of the cardiovascular system and analyze the network's ability to estimate unknown patient parameters using available patient …
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Algebraic Geometry of Bayesian Networks
… necessary theory in algebraic geometry to place Bayesian networks into the realm of algebraic statistics. This allows us to create an algebraic geometry--statistics dictionary. In particular, we study the algebraic varieties defined by the conditional independence statements of Bayesian networks. …
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Active leakage management with bayesian networks
… comprises of three models: a data adaptive Bayesian network(BN)modelforpredictingpipeleakprobabilitiesusedforleakagemonitoring, a water loss estimation model for estimating pipe leak water losses and a linear programming model in which water loss estimates and pipe leak predictions are used …
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Depicting variable elimination with Bayesian networks
… of variable elimination in discrete Bayesian networks (BNs) utilizing the BN’s directed acyclic graph (DAG) component. This includes methods representing both multiplication and marginalization operations. This graphical representation is achieved by introducing what are known as …
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Bridging Text Mining and Bayesian Networks
… using expert’s knowledge of the domain, Bayesian networks need to be updated as and when new data is observed. Literature mining is a very important source of this new data. In this work, we explore what kind of data needs to be extracted with the view to update 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 ideally …
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Applications of Bayesian networks in natural hazard assessments
… The all-round probabilistic framework of Bayesian networks constitutes an attractive alternative. In contrast to deterministic proceedings, it treats response variables as well as explanatory variables as random variables making no difference between input and output variables. Using a …
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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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Learning models of world dynamics using Bayesian networks
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
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Simplifying D-Separation and M-Separation in Bayesian Networks
… be employed while modeling and reasoning with Bayesian networks (BNs). A problem domain is modeled initially as a directed acyclic graph (DAG), denoted B, and the strengths of relationships are quanti ed by conditional probability tables (CPTs). Testing whether two sets X and Z of variables are …
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Concentration Inequalities for Dependent Random Variables on Bayesian Networks
… function defined on the random variables on a Bayesian Network. In this work, we provide several concentration inequality results under the assumption that the function is Lipshitz or bounded difference. In addition, we illustrate about the concentration of the maximum likelihood estimator of …
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Dynamic Bayesian networks for the classification of spinning discs
This thesis considers issues for the application of particle filters to a class of nonlinear filtering and classification problems. Specifically, we study a prototype system of spinning discs. The system combines linear dynamics describing rotation with a nonlinear observation model determined by …
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