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Tesis sobre el tema "Bayesian Networks"

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Publicado: 4 de junio de 2021
Última modificación: 25 de julio de 2025

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1

Horsch, Michael C. "Dynamic Bayesian networks." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/28909.

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Given the complexity of the domains for which we would like to use computers as reasoning engines, an automated reasoning process will often be required to perform under some state of uncertainty. Probability provides a normative theory with which uncertainty can be modelled. Without assumptions of independence from the domain, naive computations of probability are intractible. If probability theory is to be used effectively in AI applications, the independence assumptions from the domain should be represented explicitly, and used to greatest possible advantage. One such representation is a
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2

Bendtsen, Marcus. "Gated Bayesian Networks." Doctoral thesis, Linköpings universitet, Databas och informationsteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136761.

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Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graphical means for describing the relationships among random variables, but they also allow for (potentially) fewer parameters to estimate, and enable more efficient inference. The random variables and the relationships among them decide the structure of the directed acyclic graph that represents the Bayesian network. It is the stasis over time of these two components that we question in this thesis. By introducing a new type of probabilist
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3

Thouin, Frédéric. "Bayesian inference in networks." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=104476.

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Bayesian inference is a method that can be used to estimate an unknown and/or unobservable parameter based on evidence that is accumulated over time.In this thesis, we apply Bayesian inference techniques in the context of two network-based problems.First, we consider multi-target tracking in networks with superpositional sensors, i.e., sensors that generate measurements equal to the sum of individual contributions of each target.We derive a tractable form for a novel moment-based multi-target filter called the Additive Likelihood Moment (ALM) filter. We show, through simulations, that our par
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4

Nodelman, Uri D. "Continuous time bayesian networks /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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5

Fagundes, Moser Silva. "Integrating BDI model and Bayesian networks." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2007. http://hdl.handle.net/10183/10422.

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Individualmente, as linhas de pesquisa da Inteligência Artificial têm proposto abordagens para a resolução de inúmeros problemas complexos do mundo real. O paradigma orientado a agentes provê os agentes autônomos, capazes de perceber os seus ambientes, reagir de acordo com diferentes circunstâncias e estabelecer interações sociais com outros agentes de software ou humanos. As redes Bayesianas fornecem uma maneira de representar graficamente as distribuições de probabilidades condicionais e permitem a realização de raciocínios probabilísticos baseados em evidências. As ontologias são especifica
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6

Helldin, Tove. "Explanation Methods for Bayesian Networks." Thesis, University of Skövde, School of Humanities and Informatics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-3193.

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<p> </p><p>The international maritime industry is growing fast due to an increasing number of transportations over sea. In pace with this development, the maritime surveillance capacity must be expanded as well, in order to be able to handle the increasing numbers of hazardous cargo transports, attacks, piracy etc. In order to detect such events, anomaly detection methods and techniques can be used. Moreover, since surveillance systems process huge amounts of sensor data, anomaly detection techniques can be used to filter out or highlight interesting objects or situations to an operator. Makin
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7

Förstner, Johannes. "Optimizing Queries in Bayesian Networks." Thesis, Linköpings universitet, Databas och informationsteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-86716.

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This thesis explores and compares different methods of optimizing queries in Bayesian networks. Bayesian networks are graph-structured models that model probabilistic variables and their influences on each other; a query poses the question of what probabilities certain variables assume, given observed values on certain other variables. Bayesian inference (calculating these probabilities) is known to be NP-hard in general, but good algorithms exist in practice. Inference optimization traditionally concerns itself with finding and tweaking efficient algorithms, and leaves the choice of algorithm
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8

Suermondt, Henri Jacques. "Explanation in Bayesian belief networks." Full text available online (restricted access), 1992. http://images.lib.monash.edu.au/ts/theses/suermondt.pdf.

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9

Di, Tomaso Enza. "Soft computing for Bayesian networks." Thesis, University of Bristol, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.409531.

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10

TITO, EDISON AMERICO HUARSAYA. "BAYESIAN LEARNING FOR NEURAL NETWORKS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1999. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=14538@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>Esta dissertação investiga as Redes Neurais Bayesianas, que é uma nova abordagem que conjuga o potencial das redes neurais artificiais com a solidez analítica da estatística Bayesiana. Tipicamente, redes neurais convencionais como backpropagation, têm bom desempenho mas apresentam problemas de convergência, na ausência de dados suficientes de treinamento, ou problemas de mínimos locais, que trazem como conseqüência longo tempo de treinamento (esforço computacional) e possibilidades de sobre-treinamento (generalização ruim). Por e
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11

Roberts, Jennifer M. (Jennifer Marie). "Bayesian networks for cardiovascular monitoring." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37920.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.<br>Includes bibliographical references (p. 83-85).<br>In the Intensive Care Unit, physicians have access to many types of information when treating patients. Physicians attempt to consider as much of the relevant information as possible, but the astronomically large amounts of data collected make it impossible to consider all available information within a reasonable amount of time. In this thesis, I explore Bayesian Networks as a way to integrate patient data into a probabilistic
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12

Gomes, de Freitas João Ferdinando. "Bayesian methods for neural networks." Thesis, University of Cambridge, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.621572.

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13

Garcia-Puente, Luis David. "Algebraic Geometry of Bayesian Networks." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/11133.

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We develop the 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. A complete algebraic classification, in terms of primary decomposition of polynomial ideals, is given for Bayesian networks on at most five random variables. Hidden variables are related to the geometry of higher secant varieties. Moreover, a complete algebraic classification, in ter
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14

Huang, Yiqing. "Learning Bayesian networks guided by decomposable Markov networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0002/MQ45326.pdf.

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15

ACERBI, ENZO. "Continuos time Bayesian networks for gene networks reconstruction." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/52709.

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Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and improve the models' expressiveness. Continuous time Bayesian network
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16

Coles, Matthew David. "Bayesian network based intelligent mobility strategies for wireless sensor networks." Thesis, University of Portsmouth, 2009. https://researchportal.port.ac.uk/portal/en/theses/bayesian-network-based-intelligent-mobility-strategies-for-wireless-sensor-networks(23e8243c-d165-40c5-8838-7e8feaa8d965).html.

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This thesis is concerned with the design and analysis of new Bayesian network based mobility algorithms for mobile Wireless Sensor Networks (WSNs). The hypothesis for the work presented herein is that incorporating Artificial Intelligence (Al) at the level of the sensor nodes will improve their performance (coverage, connectivity and lifetime) and result in fault tolerance capabilities, in the face of uncertainty associated with incomplete information regarding the network. Two types of mobility strategy are presented and investigated. Firstly, a new gazing mobility strategy is presented which
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17

Bashar, Abul. "On the application of Bayesian networks for autonomic network management." Thesis, Ulster University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.646023.

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The quest for achieving an efficient, reliable and cost-effective network infrastructure in support of innovative and rich communication services has resulted in the advent and popularity of IP based converged Next Generation Networks (NGN). According to the ITU-T, the NGN has significant advantages such as support for end to end Quality of Service (QoS), generalised mobility, converged services between fixed & mobile networks and interworking with legacy networks. These networks require Network Management Systems (NMS), which play a key role in monitoring and administering them, to ensure smo
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18

Wang, Jian. "Recovering Bayesian networks with applications to gene regulatory networks." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3273725.

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19

Datta, Sagnik. "Fully bayesian structure learning of bayesian networks and their hypergraph extensions." Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2283.

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Dans cette thèse, j’aborde le problème important de l’estimation de la structure des réseaux complexes, à l’aide de la classe des modèles stochastiques dits réseaux Bayésiens. Les réseaux Bayésiens permettent de représenter l’ensemble des relations d’indépendance conditionnelle. L’apprentissage statistique de la structure de ces réseaux complexes par les réseaux Bayésiens peut révéler la structure causale sous-jacente. Il peut également servir pour la prédiction de quantités qui sont difficiles, coûteuses, ou non éthiques comme par exemple le calcul de la probabilité de survenance d’un cancer
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20

Sun, Wei. "Efficient inference for hybrid Bayesian networks." Fairfax, VA : George Mason University, 2007. http://hdl.handle.net/1920/2952.

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Thesis (Ph. D.)--George Mason University, 2007.<br>Title from PDF t.p. (viewed Jan. 22, 2008). Thesis director: KC Chang. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Vita: p. 117. Includes bibliographical references (p. 108-116). Also available in print.
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21

Caballero, Jose Louis Galan. "Modeling qualitative judgements in Bayesian networks." Thesis, Queen Mary, University of London, 2008. http://qmro.qmul.ac.uk/xmlui/handle/123456789/28170.

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Although Bayesian Networks (BNs) are increasingly being used to solve real world problems [47], their use is still constrained by the difficulty of constructing the node probability tables (NPTs). A key challenge is to construct relevant NPTs using the minimal amount of expert elicitation, recognising that it is rarely cost-effective to elicit complete sets of probability values. This thesis describes an approach to defining NPTs for a large class of commonly occurring nodes called ranked nodes. This approach is based on the doubly truncated Normal distribution with a central tendency that is
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22

Al-Kaabawi, Zainab A. A. "Bayesian hierarchical models for linear networks." Thesis, University of Plymouth, 2018. http://hdl.handle.net/10026.1/12829.

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A motorway network is handled as a linear network. The purpose of this study is to highlight dangerous motorways via estimating the intensity of accidents and study its pattern across the UK motorway network. Two mechanisms have been adopted to achieve this aim. The first, the motorway-specific intensity is estimated by modelling the point pattern of the accident data using a homogeneous Poisson process. The homogeneous Poisson process is used to model all intensities but heterogeneity across motorways is incorporated using two-level hierarchical models. The data structure is multilevel since
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23

Radice, Rosalba. "A Bayesian approach to phylogenetic networks." Thesis, University of Bath, 2011. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538163.

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Traditional phylogenetic inference assumes that the history of a set of taxa can be explained by a tree. This assumption is often violated as some biological entities can exchange genetic material giving rise to non-treelike events often called reticulations. Failure to consider these events might result in incorrectly inferred phylogenies, and further consequences, for example stagnant and less targeted drug development. Phylogenetic networks provide a flexible tool which allow us to model the evolutionary history of a set of organisms in the presence of reticulation events. In recent years,
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24

Galagali, Nikhil. "Bayesian inference of chemical reaction networks." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104253.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 189-198).<br>The development of chemical reaction models aids system design and optimization, along with fundamental understanding, in areas including combustion, catalysis, electrochemistry, and biology. A systematic approach to building reaction network models uses available data not only to estimate unknown parameters, but to also learn the model structure. Bayesian inference provides a natural approach for
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25

Morrissey, Edward R. "Bayesian inference of causal gene networks." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/45732/.

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Genes do not act alone, rather they form part of large interacting networks with certain genes regulating the activity of others. The structure of these networks is of great importance as it can produce emergent behaviour, for instance, oscillations in the expression of network genes or robustness to uctuations. While some networks have been studied in detail, most networks underpinning biological processes have not been fully characterised. Elucidating the structure of these networks is of paramount importance to understand these biological processes. With the advent of whole-genome gene expr
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26

Oates, Christopher J. "Bayesian inference for protein signalling networks." Thesis, University of Warwick, 2013. http://wrap.warwick.ac.uk/58325/.

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Cellular response to a changing chemical environment is mediated by a complex system of interactions involving molecules such as genes, proteins and metabolites. In particular, genetic and epigenetic variation ensure that cellular response is often highly specific to individual cell types, or to different patients in the clinical setting. Conceptually, cellular systems may be characterised as networks of interacting components together with biochemical parameters specifying rates of reaction. Taken together, the network and parameters form a predictive model of cellular dynamics which may be u
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27

Van, der Laarse Maryn. "Modelling rhino presence with Bayesian networks." Diss., University of Pretoria, 2020. http://hdl.handle.net/2263/73455.

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Modelling complex systems such as how the white rhinoceros Ceratotherium simum simum uses a landscape requires innovative and multi-disciplinary approaches. Bayesian networks have been shown to provide a dynamic, easily interpretable framework to represent real-world problems. This, together with advances in remote sensor technology to easily quantify environmental variables, make non-intrusive techniques for understanding and inference of ecological processes more viable than ever. However, when modelling an animal’s use of a landscape we only have access to presence locations. These data ar
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28

Sriram, Aparna. "Predicting Gene Relations Using Bayesian Networks." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1302619630.

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29

GREPPI, ALESSANDRO. "Bayesian Networks Models for Equity Market." Doctoral thesis, Università degli studi di Pavia, 2017. http://hdl.handle.net/11571/1203358.

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Financial markets evolve quickly due to the continuous innovation of investment tool and investors need to take the best decisions in the shortest time possible. This is why we propose in my thesis an innovative approach based on graphical models in order to provide to practitioners buy or sell indications on the American equity market (S&P 500). Generally, investors observe the market and consequently make a decision but this procedure is generally time consuming and do not always lead to a gain. This is why algorithmic trading is spreading through financial industry in the last years. In thi
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30

Scalabrin, Maria. "Bayesian Learning Strategies in Wireless Networks." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3424931.

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This thesis collects the research works I performed as a Ph.D. candidate, where the common thread running through all the works is Bayesian reasoning with applications in wireless networks. The pivotal role in Bayesian reasoning is inference: reasoning about what we don’t know, given what we know. When we make inference about the nature of the world, then we learn new features about the environment within which the agent gains experience, as this is what allows us to benefit from the gathered information, thus adapting to new conditions. As we leverage the gathered information, our belief abou
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31

Ewell, Cris Vincent. "Detection of Deviations From Authorized Network Activity Using Dynamic Bayesian Networks." NSUWorks, 2011. http://nsuworks.nova.edu/gscis_etd/146.

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This research addressed one of the hard problems still plaguing the information security profession; detection of network activity deviations from authorized accounts when the deviations are similar to normal network activity. Specifically, when user and administrator type accounts are used for malicious activity, harm can come to the organization. Accurately modeling normal user network activity is hard to accomplish and detecting misuse is a complex problem. Much work has been done in the past with intrusion detection systems, but being able to detect masquerade events with high accuracy and
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32

Liu, Ziying. "Identifying gene regulatory networks using Bayesian networks and domain knowledge." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27269.

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Bayesian network techniques have been used for discovering causal relationships among large number of variables in many applications. This thesis demonstrates how Bayesian techniques are used to build gene regulation networks. The contribution of this thesis is to find a novel way of combining pre-knowledge (biological domain information) into Bayesian network learning process for microarray data analysis. Such pre-knowledge includes biological process, cellular component and molecular function information and cell cycle information. Incorporating preexisting knowledge into the Bayesian networ
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33

Langseth, Helge. "Bayesian networks with applications in reliability analysis." Doctoral thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2002. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-959.

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<p>A common goal of the papers in this thesis is to propose, formalize and exemplify the use of Bayesian networks as a modelling tool in reliability analysis. The papers span work in which Bayesian networks are merely used as a modelling tool (Paper I), work where models are specially designed to utilize the inference algorithms of Bayesian networks (Paper II and Paper III), and work where the focus has been on extending the applicability of Bayesian networks to very large domains (Paper IV and Paper V).</p><p><b>Paper I </b>is in this respect an application paper, where model building, estima
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34

Cowan, Alexandra. "Modelling trader intentions through evolving Bayesian networks." Thesis, Queen's University Belfast, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.725743.

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This research highlights the problem of trade based market manipulation in financial markets, where an individual or party aim to distort the pricing mechanism and gain profit at the expense of law abiding investors. This research evaluates data mining approaches applied to financial market surveillance and addresses a current deficit in literature with regards to modelling traders at an entity level. A system is proposed, named the Evolving Bayesian Network (EBN), to model an individual trader's behaviour using transaction order data generated by the participant. The aim of the model is to in
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35

Goubanova, Olga. "Bayesian networks for predicting duration of phones." Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/29125.

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The duration of a phonetic segment (phone) is usually modelled with a database of feature vectors, that consist of a set of linguistic factors’ (attributes’). There have been a number of models developed for predicting a phone’s duration, ranging from rule-based to neural nets to classification and regression tree (CART) to sums-of-products (SoP) models duration is predicted by a decision tree. In our work, we use a Bayesian belief network (BN) consisting of discrete nodes for the linguistic factors and a single continuous node for the phone’s duration. Interactions between factors are represe
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36

Butz, C. J. "The relational database theory of Bayesian networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0016/NQ54667.pdf.

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37

Koskinen, Johan. "Essays on Bayesian Inference for Social Networks." Doctoral thesis, Stockholm : Department of Statistics [Statistiska institutionen], Univ, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-128.

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38

Cho, Hyun Cheol. "Dynamic Bayesian networks for online stochastic modeling." abstract and full text PDF (free order & download UNR users only), 2006. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3221394.

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39

Hearty, Peter Stewart. "Modelling Agile software processes using bayesian networks." Thesis, Queen Mary, University of London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.509669.

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40

Auld, Thomas James. "Bayesian applications of multilayer perceptron neural networks." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.613209.

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41

Vasilieva, Stephania. "Ontologies as Bayesian Networks for Space Debris." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/613564.

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Space debris is a rising problem in today's world. Because there is so much in space that is unknown, it is critical to eventually catalog every piece. Since there are many attributes and properties attached to space objects, it is preferable to use an ontological classification method. The information presented in the ontology can then be used to answer questions about space debris. A Bayesian network would accomplish that because of its quantitative nature. The similarities between ontologies and Bayesian networks, such as their architectures and their flexibility, make it possible to integr
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42

Osunmakinde, Isaac Olusegun. "Computational intelligent systems : evolving dynamic Bayesian networks." Doctoral thesis, University of Cape Town, 2009. http://hdl.handle.net/11427/6429.

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Includes abstract.<br>Includes bibliographical references (p. 163-172).<br>In this thesis, a new class of temporal 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 suited to achieve situation awareness, in which elements in the environment must be perceived within a volume of time and space, their meaning understood, and their status predicted in the n
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43

Butz, C. J. "The relational database theory of Bayesian networks." Ottawa : National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.nlc-bnc.ca/obj/s4/f2/dsk1/tape3/PQDD%5F0016/NQ54667.pdf.

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44

Babbar, Sakshi. "Inferring Anomalies from Data using Bayesian Networks." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9371.

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Existing studies on data mining has largely focused on the design of measures and algorithms to identify outliers in large and high dimensional categorical and numeric databases. However, not much stress has been given on the interestingness of the reported outlier. One way to ascertain interestingness and usefulness of the reported outlier is by making use of domain knowledge. In this thesis, we present measures to discover outliers based on background knowledge, represented by a Bayesian network. Using causal relationships between attributes encoded in the Bayesian framework, we demonstrate
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45

Touchard, Samuel. "Bayesian inference of gene-miRNA regulatory networks." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/8726/.

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Nowadays, in the post-genomics era, one of the major tasks and challenges is to decipher how genes are regulated. The miRNAs play an essential regulatory role in both plants and animals. It has been estimated that about 30% of the genes in the human genome are down-regulated by microRNAs (miRNAs), short RNA molecules which repress the translation of proteins of mRNAs in animals and plants. Genes which are regulated by a miRNA are called targets of this given miRNA. Hence, the task is to try to determine which miRNAs regulate which genes, in order then to build a network of these DNA components
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46

Taheri, Sona. "Learning Bayesian networks based on optimization approaches." Thesis, University of Ballarat, 2012. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/36051.

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Learning accurate classifiers from preclassified data is a very active research topic in machine learning and artifcial intelligence. There are numerous classifier paradigms, among which Bayesian Networks are very effective and well known in domains with uncertainty. Bayesian Networks are widely used representation frameworks for reasoning with probabilistic information. These models use graphs to capture dependence and independence relationships between feature variables, allowing a concise representation of the knowledge as well as efficient graph based query processing algorithms. This repr
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47

Olson, John Thomas. "Hardware/software partitioning utilizing Bayesian belief networks." Diss., The University of Arizona, 2000. http://hdl.handle.net/10150/284156.

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In heterogeneous systems design, partitioning of the functional specifications into hardware and software components is an important procedure. Often, a hardware platform is chosen and the software is mapped onto the existing partial solution, or the actual partitioning is performed in an ad hoc manner. The partitioning approach presented here is novel in that it uses Bayesian Belief Networks (BBNs) to categorize functional components into hardware and software classifications. The BBN's ability to propagate evidence permits the effects of a classification decision made about one function to b
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Back, Alexander, and William Keith. "Bayesian Neural Networks for Financial Asset Forecasting." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252562.

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Neural networks are powerful tools for modelling complex non-linear mappings, but they often suffer from overfitting and provide no measures of uncertainty in their predictions. Bayesian techniques are proposed as a remedy to these problems, as these both regularize and provide an inherent measure of uncertainty from their posterior predictive distributions. By quantifying predictive uncertainty, we attempt to improve a systematic trading strategy by scaling positions with uncertainty. Exact Bayesian inference is often impossible, and approximate techniques must be used. For this task, this th
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49

Pappaterra, Mauro José. "Implementing Bayesian Networks for online threat detection." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-86238.

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Cybersecurity threats have surged in the past decades. Experts agree that conventional security measures will soon not be enough to stop the propagation of more sophisticated and harmful cyberattacks. Recently, there has been a growing interest in mastering the complexity of cybersecurity by adopting methods borrowed from Artificial Intelligence (AI) in order to support automation. Moreover, entire security frameworks, such as DETECT (Decision Triggering Event Composer and Tracker), are designed aimed to the automatic and early detection of threats against systems, by using model analysis and
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50

Rios, Felix Leopoldo. "Bayesian structure learning in graphical models." Licentiate thesis, KTH, Matematisk statistik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-179852.

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This thesis consists of two papers studying structure learning in probabilistic graphical models for both undirected graphs anddirected acyclic graphs (DAGs). Paper A, presents a novel family of graph theoretical algorithms, called the junction tree expanders, that incrementally construct junction trees for decomposable graphs. Due to its Markovian property, the junction tree expanders are shown to be suitable for proposal kernels in a sequential Monte Carlo (SMC) sampling scheme for approximating a graph posterior distribution. A simulation study is performed for the case of Gaussian decompos
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