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Relevant bibliographies by topics / Bayesian intelligence / Dissertations / Theses

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Dissertations / Theses on the topic 'Bayesian intelligence'

Author: Grafiati

Published: 10 December 2022

Last updated: 19 July 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

Edgington, Padraic D. "Modular Bayesian filters." Thesis, University of Louisiana at Lafayette, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3712276.

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<p> In this dissertation, I introduce modularization as a means of efficiently solving problems represented by dynamic Bayesian networks and study the properties and effects of modularization relative to traditional solutions. Modularizing a Bayesian filter allows its results to be calculated faster than a traditional Bayesian filter. Traditional Bayesian filters can have issues when large problems must be solved within a short period of time. Modularization addresses this issue by dividing the full problem into a set of smaller problems that can then be solved with separate Bayesian filter

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3

Hanif, A. "Computational intelligence sequential Monte Carlos for recursive Bayesian estimation." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1403732/.

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Recursive Bayesian estimation using sequential Monte Carlos methods is a powerful numerical technique to understand latent dynamics of non-linear non-Gaussian dynamical systems. Classical sequential Monte Carlos suffer from weight degeneracy which is where the number of distinct particles collapse. Traditionally this is addressed by resampling, which effectively replaces high weight particles with many particles with high inter-particle correlation. Frequent resampling, however, leads to a lack of diversity amongst the particle set in a problem known as sample impoverishment. Traditional seque

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4

Ross, Stéphane. "Model-based Bayesian reinforcement learning in complex domains." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=21960.

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Reinforcement Learning has emerged as a useful framework for learning to perform a task optimally from experience in unknown systems. A major problem for such learning algorithms is how to balance optimally the exploration of the system, to gather knowledge, and the exploitation of current knowledge, to complete the task. Model-based Bayesian Reinforcement Learning (BRL) methods provide an optimal solution to this problem by formulating it as a planning problem under uncertainty. However, the complexity of these methods has so far limited their applicability to small and simple domains. To

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5

Gannon, Michael William. "Cruise missile proliferation : an application of Bayesian analysis to intelligence forecasting." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from the National Technical Information Service, 1992. http://handle.dtic.mil/100.2/ADA257717.

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Thesis (M.S. in National Security Affairs) Naval Postgraduate School, September 1992.<br>Thesis advisor: Edward J. Laurance. ADA257717. "September 1992". Includes bibliographical reference (p. 82-84).

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6

Luo, Zhiyuan. "A probabilistic reasoning and learning system based on Bayesian belief networks." Thesis, Heriot-Watt University, 1992. http://hdl.handle.net/10399/1490.

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7

Pomerantz, Daniel. "Designing a context dependant movie recommender: a hierarchical Bayesian approach." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=86751.

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In this thesis, we analyze a context-dependent movie recommendation system using a Hierarchical Bayesian Network. Unlike most other recommender systems which either do not consider context or do so using collaborative filtering, our approach is content-based. This allows users to individually interpret contexts or invent their own contexts and continue to get good recommendations. By using a Hierarchical Bayesian Network, we can provide context recommendations when users have only provided a small amount of information about their preferences per context. At the same time, our model has enoug

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8

Carr, S. "Investigating the applicability of bayesian networks to the analysis of military intelligence." Thesis, Cranfield University, 2008. http://hdl.handle.net/1826/2826.

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Intelligence failures have been attributed to an inability to correlate many small pieces of data into a larger picture. This thesis has sought to investigate how the fusion and analysis of uncertain or incomplete data through the use of Bayesian Belief Networks (BBN) compares with people’s intuitive judgements. These flexible, robust, graphical probabilistic networks are able to incorporate values from a wide range of sources including empirical values, experimental data and subjective values. Using the latter, elicited from a number of serving military officers, BBNs provide a logical framew

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9

Jaitha, Anant. "An Introduction to the Theory and Applications of Bayesian Networks." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1638.

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Bayesian networks are a means to study data. A Bayesian network gives structure to data by creating a graphical system to model the data. It then develops probability distributions over these variables. It explores variables in the problem space and examines the probability distributions related to those variables. It conducts statistical inference over those probability distributions to draw meaning from them. They are good means to explore a large set of data efficiently to make inferences. There are a number of real world applications that already exist and are being actively researched. Th

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10

Saini, Nishrith. "Using Machine Intelligence to Prioritise Code Review Requests." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20140.

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Background: Modern Code Review (MCR) is a process of reviewing code which is a commonly used practice in software development. It is the process of reviewing any new code changes that need to be merged with the existing codebase. As a developer, one receives many code review requests daily that need to be reviewed. When the developer receives the review requests, they are not prioritised. Manuallyprioritising them is a challenging and time-consuming process. Objectives: This thesis aims to address and solve the above issues by developing a machine intelligence-based code review prioritisation

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11

Gurney, John. "Application of naïve Bayesian artificial intelligence to referral refinement of chronic open angle glaucoma." Thesis, Aston University, 2017. http://publications.aston.ac.uk/30341/.

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The purpose of this study was to determine whether naïve Bayesian artificial intelligence could accurately predict clinical decisions made during the referral refinement of Chronic open angle glaucoma (COAG) by three specialist independent prescribing optometrists using the highly structured standard operating procedure (SOP) adopted by the Community Ophthalmology Team (COT) of the West Kent Clinical Commissioning Group (CCG). The effectiveness of the COT, in terms of reducing false positive referrals and costs to the National Health Service (NHS), was also explored. This was the first study o

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12

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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13

Roussos, Evangelos. "Bayesian methods for sparse data decomposition and blind source separation." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.589766.

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In an exploratory approach to data analysis, it is often useful to consider the observations as generated from a set of latent generators or 'sources' via a generally unknown mapping. Reconstructing sources from their mixtures is an extremely ill-posed problem in general. However, solutions to such inverse problems can, in many cases, be achieved by incorporating prior knowledge about the problem, captured in the form of constraints. This setting is a natural candidate for the application of the Bayesian method- ology, allowing us to incorporate "soft" constraints in a natural manner. This The

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14

Vural, Ickin. "Spamming mobile botnet detection using computational intelligence." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/36775.

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This dissertation explores a new challenge to digital systems posed by the adaptation of mobile devices and proposes a countermeasure to secure systems against threats to this new digital ecosystem. The study provides the reader with background on the topics of spam, Botnets and machine learning before tackling the issue of mobile spam. The study presents the reader with a three tier model that uses machine learning techniques to combat spamming mobile Botnets. The three tier model is then developed into a prototype and demonstrated to the reader using test scenarios. Finally, this dis

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15

Ceccon, Stefano. "Extending Bayesian network models for mining and classification of glaucoma." Thesis, Brunel University, 2013. http://bura.brunel.ac.uk/handle/2438/8051.

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Glaucoma is a degenerative disease that damages the nerve fiber layer in the retina of the eye. Its mechanisms are not fully known and there is no fully-effective strategy to prevent visual impairment and blindness. However, if treatment is carried out at an early stage, it is possible to slow glaucomatous progression and improve the quality of life of sufferers. Despite the great amount of heterogeneous data that has become available for monitoring glaucoma, the performance of tests for early diagnosis are still insufficient, due to the complexity of disease progression and the diffculties in

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16

Groves, Adrian R. "Bayesian learning methods for modelling functional MRI." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:fe46e696-a1a6-4a9d-9dfe-861b05b1ed33.

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Bayesian learning methods are the basis of many powerful analysis techniques in neuroimaging, permitting probabilistic inference on hierarchical, generative models of data. This thesis primarily develops Bayesian analysis techniques for magnetic resonance imaging (MRI), which is a noninvasive neuroimaging tool for probing function, perfusion, and structure in the human brain. The first part of this work fits nonlinear biophysical models to multimodal functional MRI data within a variational Bayes framework. Simultaneously-acquired multimodal data contains mixtures of different signals and ther

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17

Rey, Alexandre Del. "Inteligência dinâmica nas organizações: a utilização de redes bayesianas na redução de incertezas nos processos de inteligência competitiva." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/12/12139/tde-30032012-175423/.

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O objetivo da dissertação é explorar Redes Bayesianas como ferramenta para reduzir incertezas nos processos de Inteligência Competitiva. Nela, através da revisão de conceitos de Planejamento Estratégico, Tomada de Decisão, Inteligência Competitiva e da capacidade de inferência de Redes Bayesianas é proposta uma abordagem de utilização destas redes com este intuito. Para tanto um estudo de caso apresenta o passo a passo da implementação da abordagem proposta em um ambiente simulado de gestão. No estudo de caso, cada uma das etapas da modelagem de cenários é descrita em detalhes, salientando os

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18

Yu, Shen. "A Bayesian machine learning system for recognizing group behaviour." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:8881/R/?func=dbin-jump-full&object_id=32565.

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19

Chakar, Joseph. "Advanced Photovoltaic Degradation Analysis using Physics Modeling and Bayesian Machine Learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAX120.

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L’avenir de l'énergie solaire s’annonce radieux. Cependant, la réalisation de ces prévisions dépend de notre capacité à comprendre et à limiter l'impact de la dégradation sur les performances. Qu'il s'agisse de technologies classiques à base de silicium ou de technologies émergentes comme les pérovskites, les cellules solaires restent vulnérables à différents facteurs internes et externes pouvant compromettre leurs performances et, par conséquent, leur potentiel commercial. Bien que des techniques expérimentales permettent d'étudier la dégradation, leur usage reste restreint par leurs coûts él

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20

Gopalakrishnan, Arjun. "Probabilistic Analysis of Contracting Ebola Virus Using Contextual Intelligence." Thesis, University of North Texas, 2017. https://digital.library.unt.edu/ark:/67531/metadc984182/.

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The outbreak of the Ebola virus was declared a Public Health Emergency of International Concern by the World Health Organisation (WHO). Due to the complex nature of the outbreak, the Centers for Disease Control and Prevention (CDC) had created interim guidance for monitoring people potentially exposed to Ebola and for evaluating their intended travel and restricting the movements of carriers when needed. Tools to evaluate the risk of individuals and groups of individuals contracting the disease could mitigate the growing anxiety and fear. The goal is to understand and analyze the nature of ris

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21

Abu-Hakmeh, Khaldoon Emad. "Assessing the use of voting methods to improve Bayesian network structure learning." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45826.

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Structure inference in learning Bayesian networks remains an active interest in machine learning due to the breadth of its applications across numerous disciplines. As newer algorithms emerge to better handle the task of inferring network structures from observational data, network and experiment sizes heavily impact the performance of these algorithms. Specifically difficult is the task of accurately learning networks of large size under a limited number of observations, as often encountered in biological experiments. This study evaluates the performance of several leading structure learning

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22

Dearden, Richard W. "Learning and planning in structured worlds." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0020/NQ56531.pdf.

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23

Pieropan, Alessandro. "Structure learning of graphical models for task-oriented robot grasping." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2010. http://amslaurea.unibo.it/1641/.

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In the collective imaginaries a robot is a human like machine as any androids in science fiction. However the type of robots that you will encounter most frequently are machinery that do work that is too dangerous, boring or onerous. Most of the robots in the world are of this type. They can be found in auto, medical, manufacturing and space industries. Therefore a robot is a system that contains sensors, control systems, manipulators, power supplies and software all working together to perform a task. The development and use of such a system is an active area of research and one of the mai

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24

Aguiar, Sandra da Cruz Garcia do Espírito Santo. "Previsão do preço da Commodity do Butadieno a partir do uso de redes Bayesianas." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/127232.

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As teorias que sustentam os modelos de precificação têm obtido resultados pouco satisfatórios ou insatisfatórios, uma vez que em cada estudo busca aproximar-se da realidade por apenas uma face, não observando o problema de todos os ângulos. Nesse sentido, percebeu-se um gap nos estudos de previsão, explorar sob outras lentes a dinâmica das variáveis do mercado que influenciam a formação do preço para o seu prévio monitoramento. Assim, o objetivo desta pesquisa foi construir uma ferramenta de apoio à decisão que pudesse prever, periodicamente, o preço futuro de uma commodity a curto e médio pra

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Karaaslan, Hatice. "A Study Of Argumentation In Turkish Within A Bayesian Reasoning Framework: Arguments From Ignorance." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614858/index.pdf.

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In this dissertation, a normative prescriptive paradigm, namely a Bayesian theory of content-dependent argument strength, was employed in order to investigate argumentation, specifically the classic fallacy of the &ldquo<br>argument from ignorance&rdquo<br>or &ldquo<br>argumentum ad ignorantiam&rdquo<br>. The study was carried out in Turkish with Turkish participants. In the Bayesian framework, argument strength is determined by the interactions between three major factors: prior belief, polarity, and evidence reliability. In addition, topic effects are considered. Three experiments were condu

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Jambeiro, Filho Jorge Eduardo de Schoucair. "Tratamento bayesiano de interações entre atributos de alta cardinalidade." [s.n.], 2007. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276204.

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Orientador: Jacques Wainer<br>Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação<br>Made available in DSpace on 2018-08-09T21:11:41Z (GMT). No. of bitstreams: 1 JambeiroFilho_JorgeEduardodeSchoucair_D.pdf: 736285 bytes, checksum: b7d7f186f743f9b0e541c857b0ca8226 (MD5) Previous issue date: 2007<br>Resumo: Analisamos o uso de métodos Bayesianos em um problema de classificação de padrões de interesse prático para a Receita Federal do Brasil que é caracterizado pela presença de atributos de alta cardinalidade e pela existência de interações relevantes entre eles. Mostr

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27

Elshamy, Wesam Samy. "Continuous-time infinite dynamic topic models." Diss., Kansas State University, 2012. http://hdl.handle.net/2097/15176.

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Doctor of Philosophy<br>Department of Computing and Information Sciences<br>William Henry Hsu<br>Topic models are probabilistic models for discovering topical themes in collections of documents. In real world applications, these models provide us with the means of organizing what would otherwise be unstructured collections. They can help us cluster a huge collection into different topics or find a subset of the collection that resembles the topical theme found in an article at hand. The first wave of topic models developed were able to discover the prevailing topics in a big collection of

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Hernandes, André Carmona. "Análise de risco de colisão usando redes bayesianas." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/18/18149/tde-05112013-162021/.

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A segurança no tráfego de carros é um assunto em foco nos dias de hoje e, dentro dele, podem-se citar os sistemas de auxílio ao motorista que vêm sendo desenvolvidos com a finalidade de reduzir o grande número de fatalidades em acidentes de trânsito. Tais sistemas de auxílio buscam mitigar falhas humanas como falta de atenção e imprudência. Visto isso, o projeto SENA, desenvolvido pelo Laboratório de Robótica Móvel da Escola de Engenharia de São Carlos, busca contribuir com a evolução dessa assistência ao motorista. O presente trabalho realiza um estudo sobre uma técnica de inteligência artifi

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Meganck, Stijn. "Towards an Integral Approach for Modeling Causality." Phd thesis, INSA de Rouen, 2008. http://tel.archives-ouvertes.fr/tel-00915256.

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A partir de données d'observation classiques, il est rarement possible d'arriver à une structure de réseau bayésien qui soit complètement causale. Le point théorique auquel nous nous intéressons est l'apprentissage des réseaux bayésiens causaux, avec ou sans variables latentes. Nous nous sommes d'abord focalisés sur la découverte de relations causales lorsque toutes les variables sont connues (i.e. il n'y a pas de variables latentes) en proposant un algorithme d'apprentissage utilisant à la fois des données issues d'observations et d'expérimentations. Logiquement, nous nous sommes ensuite conc

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Suvorov, Anton. "Molecular Evolution of Odonata Opsins, Odonata Phylogenomics and Detection of False Positive Sequence Homology Using Machine Learning." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7320.

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My dissertation comprises three related topics of evolutionary and computational biology, which correspond to the three Chapters. Chapter 1 focuses on tempo and mode of evolution in visual genes, namely opsins, via duplication events and subsequent molecular adaptation in Odonata (dragonflies and damselflies). Gene duplication plays a central role in adaptation to novel environments by providing new genetic material for functional divergence and evolution of biological complexity. Odonata have the largest opsin repertoire of any insect currently known. In particular our results suggest that bo

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31

Howell, David R. "Finding needles in a haystack a resource allocation methodology to design strategies to detect terrorist weapon development /." Santa Monica, CA : RAND, 2009. http://www.rand.org/pubs/rgs_dissertations/2009/RAND_RGSD247.pdf.

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Thesis (Ph.D.)--RAND Graduate School, 2009.<br>"This document was submitted as a dissertation in June 2009 in partial fulfillment of the requirements of the doctoral degree in public policy analysis at the Pardee RAND Graduate School. The faculty committee that supervised and approved the dissertation consisted of Gregory F. Treverton (Chair), Lynn E. Davis, David E. Mosher, and Walter L. Perry. Professor Kathryn Blackmond Laskey (George Mason University) was the external reader. Financial support for this dissertation was provided by RAND's National Defense Research Institute"--Cover. Title f

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Azevedo, Carlos Renato Belo 1984. "Anticipation in multiple criteria decision-making under uncertainty = Antecipação na tomada de decisão com múltiplos critérios sob incerteza." [s.n.], 2012. http://repositorio.unicamp.br/jspui/handle/REPOSIP/260775.

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Orientador: Fernando José Von Zuben<br>Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação<br>Made available in DSpace on 2018-08-26T06:49:07Z (GMT). No. of bitstreams: 1 Azevedo_CarlosRenatoBelo_D.pdf: 3449858 bytes, checksum: 7a1811aa772f1ae996e8851c60627b7c (MD5) Previous issue date: 2012<br>Resumo: A presença de incerteza em resultados futuros pode levar a indecisões em processos de escolha, especialmente ao elicitar as importâncias relativas de múltiplos critérios de decisão e de desempenhos de curto vs. longo prazo. Algumas decisões, n

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Nogueira, Claudia Mendes. "Dificuldades orçamentárias básicas das famílias brasileiras: um convite à reflexão a partir de redes bayesianas." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/12/12139/tde-03122012-193052/.

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Este estudo visa compreender a adequação dos rendimentos às necessidades e condições de vida dos brasileiros. Observando os dados da Pesquisa de Orçamentos Familiares (POF) realizada pelo IBGE (Instituto Brasileiro de Geografia e Estatística) para o período: 2008 e 2009, o estudo identifica um modelo que se concentra na investigação sobre o fato de 75% dos domicílios brasileiros declararem dificuldades orçamentárias. Para desenvolver um modelo, foi utilizada a percepção declarada e subjetiva de adequação da renda, informada pelo chefe de família ou pessoa de referência no domicílio. O referenc

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Menart, Christopher J. Menart. "Global-Context Refinement for Semantic Image Segmentation." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1523462175806808.

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Coursey, Kino High. "An Approach Towards Self-Supervised Classification Using Cyc." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5470/.

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Due to the long duration required to perform manual knowledge entry by human knowledge engineers it is desirable to find methods to automatically acquire knowledge about the world by accessing online information. In this work I examine using the Cyc ontology to guide the creation of Naïve Bayes classifiers to provide knowledge about items described in Wikipedia articles. Given an initial set of Wikipedia articles the system uses the ontology to create positive and negative training sets for the classifiers in each category. The order in which classifiers are generated and used to test articles

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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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Ben, Mrad Ali. "Observations probabilistes dans les réseaux bayésiens." Thesis, Valenciennes, 2015. http://www.theses.fr/2015VALE0018/document.

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Dans un réseau bayésien, une observation sur une variable signifie en général que cette variable est instanciée. Ceci signifie que l’observateur peut affirmer avec certitude que la variable est dans l’état signalé. Cette thèse porte sur d’autres types d’observations, souvent appelées observations incertaines, qui ne peuvent pas être représentées par la simple affectation de la variable. Cette thèse clarifie et étudie les différents concepts d’observations incertaines et propose différentes applications des observations incertaines dans les réseaux bayésiens.Nous commençons par dresser un état

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Bonnet, Djohan. "Synaptic uncertainty for in-memory computing." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST037.

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La forte demande pour les réseaux de neurones artificiels, combinée à leur coût énergétique élevé dû au "memory bottleneck" des architectures de von-Neumann, a intensifié les efforts pour surmonter les contraintes du matériel informatique. Cette thèse explore le domaine interdisciplinaire du calcul neuromorphique, en se concentrant sur des études expérimentales, des algorithmes et la conception de circuit intégrés. Dans la partie expérimentale, les caractéristiques électriques des mémoires vives basées sur la transition métal-oxyde (OxRAMs) et les mémoires à changement de phase (PCMs) sont étu

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Medina, Oliva Gabriela. "Modélisation conjointe des connaissances multi-points de vue d'un système industriel et de son système de soutien pour l'évaluation des stratégies de maintenance." Thesis, Nancy 1, 2011. http://www.theses.fr/2011NAN10092/document.

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Par rapport aux exigences de plus en plus importantes relatives au Maintien en Condition Opérationnelle d'un système industriel, le processus de maintenance joue un rôle fondamental pour l'amélioration de la disponibilité, de la productivité, etc. Pour essayer de contrôler au mieux ces performances, les responsables de maintenance doivent donc être capables de choisir les stratégies de maintenance et les ressources à mettre en oeuvre les plus adaptées aux besoins. Dans un objectif d'aide à la prise de décisions en maintenance, les travaux présentés dans ce mémoire ont pour objet de proposer un

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Dumont, Julien. "Systèmes multi-agent pour le diagnostic pluri-disciplinaire." Phd thesis, Ecole Nationale Supérieure des Mines de Saint-Etienne, 2011. http://tel.archives-ouvertes.fr/tel-00844133.

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Ce travail de recherche est consacré à la formalisation et à la réalisation d'un processus de diagnostic pluridisplinaire. La particularité d'un tel diagnostic résulte du fait qu'il nécessite de nombreux spécialistes, chacun ayant des connaissances sur leur domaine. Le problème principal réside dans les interconnexions entre les domaines. Ces interconnexions peuvent ou non être connues et influer sur le diagnostic. Dans ce manuscrit, nous proposons de réaliser un diagnostic pluridisciplinaire l'aide d'un système multi-agents. Les agents élaborent un diagnostic local à un domaine puis, fusionne

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Medina, Oliva Gabriela. "Modélisation conjointe des connaissances multi-points de vue d'un système industriel et de son système de soutien pour l'évaluation des stratégies de maintenance." Electronic Thesis or Diss., Nancy 1, 2011. http://www.theses.fr/2011NAN10092.

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Par rapport aux exigences de plus en plus importantes relatives au Maintien en Condition Opérationnelle d'un système industriel, le processus de maintenance joue un rôle fondamental pour l'amélioration de la disponibilité, de la productivité, etc. Pour essayer de contrôler au mieux ces performances, les responsables de maintenance doivent donc être capables de choisir les stratégies de maintenance et les ressources à mettre en oeuvre les plus adaptées aux besoins. Dans un objectif d'aide à la prise de décisions en maintenance, les travaux présentés dans ce mémoire ont pour objet de proposer un

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Ben, Messaoud Montassar. "SemCaDo : une approche pour la découverte de connaissances fortuites et l'évolution ontologique." Phd thesis, Université de Nantes, 2012. http://tel.archives-ouvertes.fr/tel-00716128.

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En réponse au besoin croissant de réutiliser les connaissances déjà existantes lors de l'apprentissage des réseaux bayésiens causaux, les connaissances sémantiques contenues dans les ontologies de domaine présentent une excellente alternative pour assister le processus de découverte causale avec le minimum de coût et d'eff ort. Dans ce contexte, la présente thèse s'intéresse plus particulièrement au crossing-over entre les réseaux bayésiens causaux et les ontologies et établit les bases théoriques d'une approche cyclique intégrant les deux formalismes de manière interchangeable. En premier lie

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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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Jouffroy, Emma. "Développement de modèles non supervisés pour l'obtention de représentations latentes interprétables d'images." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0050.

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Le Laser Mégajoule (LMJ) est un instrument d’envergure qui simule des conditions de pression et de température similaires à celles des étoiles. Lors d’expérimentations, plusieurs diagnostics sont guidés dans la chambre d’expériences et il est essentiel qu’ils soient positionnés de manière précise. Afin de minimiser les risques liés à l’erreur humaine dans un tel contexte expérimental, la mise en place d'un système anti-collision automatisé est envisagée. Cela passe par la conception d’outils d’apprentissage automatique offrant des niveaux de décision fiables à partir de l’interprétation d’imag

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Campbell, Glenn G. "MediaHub, Bayesian Decision-making in an Intelligent Multimodal Distributed Platform Hub." Thesis, University of Ulster, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.503741.

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Gravem, Anne-Marit. "Integrating Case-based and Bayesian Reasoning for Decision Support." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11834.

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In this thesis, we present an approach to integration of case-based reasoning and Bayesian reasoning for decision support. Our design is meant to provide physicians with decision support in the context of palliative care for lung cancer patients. Because of delays in the medical data, we created an intermediate application with the aim to assist people in choosing an adequate wine for a given meal. We have developed a system that is able to utilize both the general knowledge of the Bayesian network and the specialized knowledge of the case base. Our results shows that the combination of CBR an

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Schlenoff, Craig. "Inferring intentions through state representations in cooperative human-robot environments." Thesis, Dijon, 2014. http://www.theses.fr/2014DIJOS064/document.

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Les humains et les robots travaillant en toute sécurité et en parfaite harmonie dans un environnement est l'un des objectifs futurs de la communauté robotique. Quand les humains et les robots peuvent travailler ensemble dans le même espace, toute une catégorie de tâches devient prête à l'automatisation, allant de la collaboration pour l'assemblage de pièces, à la manutention de pièces et de materiels ainsi qu'à leur livraison. Garantir la sûreté des humains nécessite que le robot puisse être capable de surveiller la zone de travail, déduire l'intention humaine, et être conscient suffisamment t

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Kim, Jong Hwan. "Autonomous Navigation, Perception and Probabilistic Fire Location for an Intelligent Firefighting Robot." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64997.

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Firefighting robots are actively being researched to reduce firefighter injuries and deaths as well as increase their effectiveness on performing tasks. There has been difficulty in developing firefighting robots that autonomously locate a fire inside of a structure that is not in the direct robot field of view. The commonly used sensors for robots cannot properly function in fire smoke-filled environments where high temperature and zero visibility are present. Also, the existing obstacle avoidance methods have limitations calculating safe trajectories and solving local minimum problem while a

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Jackson, Arthur Rhydon. "Predicting Flavonoid UGT Regioselectivity with Graphical Residue Models and Machine Learning." Digital Commons @ East Tennessee State University, 2009. https://dc.etsu.edu/etd/1820.

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Machine learning is applied to a challenging and biologically significant protein classification problem: the prediction of flavonoid UGT acceptor regioselectivity from primary protein sequence. Novel indices characterizing graphical models of protein residues are introduced. The indices are compared with existing amino acid indices and found to cluster residues appropriately. A variety of models employing the indices are then investigated by examining their performance when analyzed using nearest neighbor, support vector machine, and Bayesian neural network classifiers. Improvements over

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Qiu, Yumeng. "Leveraging Influential Factors into Bayesian Knowledge Tracing." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-theses/53.

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Predicting student performance is an important part of the student modeling task in Intelligent Tutoring System (ITS). The state-of-art model for predicting student performance - Bayesian Knowledge Tracing (KT) has many critical limitations. One specific limitation is that KT has no underlying mechanism for memory decay represented in the model, which means that no forgetting is happening in the learning process. In addition we notice that numerous modification to the KT model have been proposed and evaluated, however many of these are often based on a combination of intuition and experience i

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