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Thèses sur le sujet « Continuous Time Bayesian Networks »

Auteur : Grafiati

Publié le 9 mars 2023

Mis à jour le 31 juillet 2025

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1

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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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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CODECASA, DANIELE. "Continuous time bayesian network classifiers." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/80691.

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Streaming data are relevant to finance, computer science, and engineering, while they are becoming increasingly important to medicine and biology. Continuous time Bayesian networks are designed for analyzing efficiently multivariate streaming data, exploiting the conditional independencies in continuous time homogeneous Markov processes. Continuous time Bayesian network classifiers are a specialization of continuous time Bayesian networks designed for multivariate streaming data classification when time duration of events matters and the class occurs in the future. Continuous time Bayesian net

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VILLA, SIMONE. "Continuous Time Bayesian Networks for Reasoning and Decision Making in Finance." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/69953.

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L'analisi dell'enorme quantità di dati finanziari, messi a disposizione dai mercati elettronici, richiede lo sviluppo di nuovi modelli e tecniche per estrarre efficacemente la conoscenza da utilizzare in un processo decisionale informato. Lo scopo della tesi concerne l'introduzione di modelli grafici probabilistici utilizzati per il ragionamento e l'attività decisionale in tale contesto. Nella prima parte della tesi viene presentato un framework che utilizza le reti Bayesiane per effettuare l'analisi e l'ottimizzazione di portafoglio in maniera olistica. In particolare, esso sfrutta, da un l

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Fan, Yu. "Continuous time Bayesian Network approximate inference and social network applications." Diss., [Riverside, Calif.] : University of California, Riverside, 2009. http://proquest.umi.com/pqdweb?index=0&did=1957308751&SrchMode=2&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268330625&clientId=48051.

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Thesis (Ph. D.)--University of California, Riverside, 2009.<br>Includes abstract. Title from first page of PDF file (viewed March 8, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 130-133). Also issued in print.

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GATTI, ELENA. "Graphical models for continuous time inference and decision making." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2011. http://hdl.handle.net/10281/19575.

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Reasoning about evolution of system in time is both an important and challenging task. We are interested in probability distributions over time of events where often observations are irregularly spaced over time. Probabilistic models have been widely used to accomplish this task but they have some limits. Indeed, Hidden Markov Models and Dynamic Bayesian Networks in general require the specification of a time granularity between consecutive observations. This requirement leads to computationally inefficient learning and inference procedures when the adopted time granularity is finer than the

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Alharbi, Randa. "Bayesian inference for continuous time Markov chains." Thesis, University of Glasgow, 2019. http://theses.gla.ac.uk/40972/.

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Continuous time Markov chains (CTMCs) are a flexible class of stochastic models that have been employed in a wide range of applications from timing of computer protocols, through analysis of reliability in engineering, to models of biochemical networks in molecular biology. These models are defined as a state system with continuous time transitions between the states. Extensive work has been historically performed to enable convenient and flexible definition, simulation, and analysis of continuous time Markov chains. This thesis considers the problem of Bayesian parameter inference on these mo

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Parton, Alison. "Bayesian inference for continuous-time step-and-turn movement models." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/20124/.

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This thesis concerns the statistical modelling of animal movement paths given observed GPS locations. With observations being in discrete time, mechanistic models of movement are often formulated as such. This popularity remains despite an inability to compare analyses through scale invariance and common problems handling irregularly timed observations. A natural solution is to formulate in continuous time, yet uptake of this has been slow, often excused by a difficulty in interpreting the ‘instantaneous’ parameters associated with a continuous-time model. The aim here was to bolster usage by

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Tucker, Allan Brice James. "The automatic explanation of Multivariate Time Series with large time lags." Thesis, Birkbeck (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246924.

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CRISTINI, ALESSANDRO. "Continuous-time spiking neural networks: paradigm and case studies." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2014. http://hdl.handle.net/2108/202297.

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In the last decades many neuron models have been proposed in order to emulate the spiking behavior of the cortical neurons, from the simplest Integrateand- Fire to the most bio-realistic Hodgkin-Huxley model. The choice of which model have to be used depends on the trade-off between bio-plausibility and computational cost, that may be related to the specific purpose. The modeling of a continuous-time spiking neural network is the main purpose of this thesis. The “continuous-time” term refers to the fact that a spike can occur at any given time, thus in order to do exact computations wi

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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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Thomas, Zachary Micah. "Bayesian Hierarchical Space-Time Clustering Methods." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1435324379.

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Acciaroli, Giada. "Calibration of continuous glucose monitoring sensors by time-varying models and Bayesian estimation." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3425746.

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Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical devices that provide frequent (e.g., 1-5 min sampling rate) real-time measurements of glucose concentration for several consecutive days. This can be of great help in the daily management of diabetes. Most of the CGM systems commercially available today have a wire-based electrochemical sensor, usually placed in the subcutaneous tissue, which measures a "raw" electrical current signal via a glucose-oxidase electrochemical reaction. Observations of the raw electrical signal are frequently revealed by the sensor

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Howells, Timothy Paul. "Pattern recognition in physiological time-series data using Bayesian neural networks." Thesis, University of Edinburgh, 2003. http://hdl.handle.net/1842/24717.

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This thesis describes the application of Bayesian techniques to the analysis of a large database of physiological time series data collected during the management of patients following traumatic brain injury at the Western General Hospital in Edinburgh. The study can be divided into three main sections: • <i>Model validation using simulated data:</i> Techniques are developed that show that under certain conditions the distribution of network outputs generated by these Bayesian neural networks correctly models the desired conditional probability density functions for a wide range of simple pr

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Murray, Lawrence. "Bayesian learning of continuous time dynamical systems with applications in functional magnetic resonance imaging." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/4157.

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Temporal phenomena in a range of disciplines are more naturally modelled in continuous-time than coerced into a discrete-time formulation. Differential systems form the mainstay of such modelling, in fields from physics to economics, geoscience to neuroscience. While powerful, these are fundamentally limited by their determinism. For the purposes of probabilistic inference, their extension to stochastic differential equations permits a continuous injection of noise and uncertainty into the system, the model, and its observation. This thesis considers Bayesian filtering for state and parameter

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Dodd, Tony. "Prior knowledge for time series modelling." Thesis, University of Southampton, 2000. https://eprints.soton.ac.uk/254110/.

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Anishchenko, Anastasiia [Verfasser], and Oliver [Akademischer Betreuer] Mülken. "Efficiency of continuous-time quantum walks: from networks with disorder to deterministic fractals." Freiburg : Universität, 2015. http://d-nb.info/1122592876/34.

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Arastuie, Makan. "Generative Models of Link Formation and Community Detection in Continuous-Time Dynamic Networks." University of Toledo / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596718772873086.

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Shaikh, A. D. "Modelling data and voice traffic over IP networks using continuous-time Markov models." Thesis, Aston University, 2009. http://publications.aston.ac.uk/15385/.

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Common approaches to IP-traffic modelling have featured the use of stochastic models, based on the Markov property, which can be classified into black box and white box models based on the approach used for modelling traffic. White box models, are simple to understand, transparent and have a physical meaning attributed to each of the associated parameters. To exploit this key advantage, this thesis explores the use of simple classic continuous-time Markov models based on a white box approach, to model, not only the network traffic statistics but also the source behaviour with respect to the ne

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Burchett, Woodrow. "Improving the Computational Efficiency in Bayesian Fitting of Cormack-Jolly-Seber Models with Individual, Continuous, Time-Varying Covariates." UKnowledge, 2017. http://uknowledge.uky.edu/statistics_etds/27.

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The extension of the CJS model to include individual, continuous, time-varying covariates relies on the estimation of covariate values on occasions on which individuals were not captured. Fitting this model in a Bayesian framework typically involves the implementation of a Markov chain Monte Carlo (MCMC) algorithm, such as a Gibbs sampler, to sample from the posterior distribution. For large data sets with many missing covariate values that must be estimated, this creates a computational issue, as each iteration of the MCMC algorithm requires sampling from the full conditional distributions of

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Sahin, Elvan. "Discrete-Time Bayesian Networks Applied to Reliability of Flexible Coping Strategies of Nuclear Power Plants." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103817.

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The Fukushima Daiichi accident prompted the nuclear community to find a new solution to reduce the risky situations in nuclear power plants (NPPs) due to beyond-design-basis external events (BDBEEs). An implementation guide for diverse and flexible coping strategies (FLEX) has been presented by Nuclear Energy Institute (NEI) to manage the challenge of BDBEEs and to enhance reactor safety against extended station blackout (SBO). To assess the effectiveness of FLEX strategies, probabilistic risk assessment (PRA) methods can be used to calculate the reliability of such systems. Due to the uniquen

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Hill, Laura Anne. "Bayesian networks for modelling time : with an application for modelling survival for gene expression data." Thesis, Queen's University Belfast, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527815.

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23

Lebre, Sophie. "Stochastic process analysis for Genomics and Dynamic Bayesian Networks inference." Phd thesis, Université d'Evry-Val d'Essonne, 2007. http://tel.archives-ouvertes.fr/tel-00260250.

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This thesis is dedicated to the development of statistical and computational methods for the analysis of DNA sequences and gene expression time series.<br /><br />First we study a parsimonious Markov model called Mixture Transition Distribution (MTD) model which is a mixture of Markovian transitions. The overly high number of constraints on the parameters of this model hampers the formulation of an analytical expression of the Maximum Likelihood Estimate (MLE). We propose to approach the MLE thanks to an EM algorithm. After comparing the performance of this algorithm to results from the litter

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Hamilton, Benjamin Russell. "Applications of bayesian filtering in wireless networks: clock synchronization, localization, and rf tomography." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44707.

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In this work, we investigate the application of Bayesian filtering techniques such as Kalman Filtering and Particle filtering to the problems of network time synchronization, self-localization and radio-frequency (RF) tomography in wireless networks. Networks of large numbers of small, cheap, mobile wireless devices have shown enormous potential in applications ranging from intrusion detection to environmental monitoring. These applications require the devices to have accurate time and position estimates, however traditional techniques may not be available. Additionally RF tomography offers a

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Ziegler-Barranco, Ana, Luis Mera-Barco, Vidal Aramburu-Rojas, Carlos Raymundo, Nestor Mamani-Macedo, and Francisco Dominguez. "SCAT Model Based on Bayesian Networks for Lost-Time Accident Prevention and Rate Reduction in Peruvian Mining Operations." Springer, 2020. http://hdl.handle.net/10757/656168.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.<br>Several factors affect the activities of the mining industry. For example, accident rates are critical because they affect company ratings in the stock market (Standard & Poors). Considering that the corporate image is directly related to its stakeholders, this study conducts an accident analysis using quantitative and qualitative methods. In this way, the contingency rate is controlled, mitigated, and prevented while serving the needs) of the

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Wu, Xinying. "Reliability Assessment of a Continuous-state Fuel Cell Stack System with Multiple Degrading Components." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1556794664723115.

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Lee, Joon-Hee. "Rank-Date Distribution Method (R-D Method) For Daily Time-Series Bayesian Networks And Total Maximum Daily Load Estimation." DigitalCommons@USU, 2008. https://digitalcommons.usu.edu/etd/132.

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Daily time series-based models are required to estimate the higher frequency fluctuations of nutrient loads and concentrations. Some mechanistic mathematical models can provide daily time series outputs of nutrient concentrations but it is difficult to incorporate non-numerical data, such as management scenarios, to mechanistic mathematical models. Bayesian networks (BNs) were designed to accept and process inputs of varied types of both numerical and non-numerical inputs. A Rank-Data distribution method (R-D method) was developed to provide large time series of daily predicted flows and Total

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Mroszczyk, Przemyslaw. "Computation with continuous mode CMOS circuits in image processing and probabilistic reasoning." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/computation-with-continuous-mode-cmos-circuits-in-image-processing-and-probabilistic-reasoning(57ae58b7-a08c-4a67-ab10-5c3a3cf70c09).html.

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The objective of the research presented in this thesis is to investigate alternative ways of information processing employing asynchronous, data driven, and analogue computation in massively parallel cellular processor arrays, with applications in machine vision and artificial intelligence. The use of cellular processor architectures, with only local neighbourhood connectivity, is considered in VLSI realisations of the trigger-wave propagation in binary image processing, and in Bayesian inference. Design issues, critical in terms of the computational precision and system performance, are exten

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Lenz, Lutz Henning. "Automatic Tuning of Integrated Filters Using Neural Networks." PDXScholar, 1993. https://pdxscholar.library.pdx.edu/open_access_etds/4604.

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Component values of integrated filters vary considerably due to· manufacturing tolerances and environmental changes. Thus it is of major importance that the components of an integrated filter be electronically tunable. The method explored in this thesis is the transconductance-C-method. A method of realizing higher-order filters is to use a cascade structure of second-order filters. In this context, a method of tuning second-order filters becomes important The research objective of this thesis is to determine if the Neural Network methodology can be used to facilitate the filter tuning process

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Selent, Douglas A. "Creating Systems and Applying Large-Scale Methods to Improve Student Remediation in Online Tutoring Systems in Real-time and at Scale." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/308.

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"A common problem shared amongst online tutoring systems is the time-consuming nature of content creation. It has been estimated that an hour of online instruction can take up to 100-300 hours to create. Several systems have created tools to expedite content creation, such as the Cognitive Tutors Authoring Tool (CTAT) and the ASSISTments builder. Although these tools make content creation more efficient, they all still depend on the efforts of a content creator and/or past historical. These tools do not take full advantage of the power of the crowd. These issues and challenges faced by on

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Jagannathan, Ramanujan. "Evaluation of Crossover Displaced Left-turn (XDL) Intersections and Real-time Signal Control Strategies with Artificial Intelligence Techniques." Thesis, Virginia Tech, 2003. http://hdl.handle.net/10919/10144.

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Although concepts of the XDL intersection or CFI (Continuous Flow Intersection) have been around for approximately four decades, users do not yet have a simplified procedure to evaluate its traffic performance and compare it with a conventional intersection. Several studies have shown qualitative and quantitative benefits of the XDL intersection without providing accessible tools for traffic engineers and planners to estimate average control delays, and queues. Modeling was conducted on typical geometries over a wide distribution of traffic flow conditions for three different design configurat

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Iacopini, Matteo. "Essays on econometric modelling of temporal networks." Thesis, Paris 1, 2018. http://www.theses.fr/2018PA01E058/document.

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La théorie des graphes a longtemps été étudiée en mathématiques et en probabilité en tant qu’outil pour décrire la dépendance entre les nœuds. Cependant, ce n’est que récemment qu’elle a été mise en œuvre sur des données, donnant naissance à l’analyse statistique des réseaux réels.La topologie des réseaux économiques et financiers est remarquablement complexe: elle n’est généralement pas observée, et elle nécessite ainsi des procédures inférentielles adéquates pour son estimation, d’ailleurs non seulement les nœuds, mais la structure de la dépendance elle-même évolue dans le temps. Des outils

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Romano, Michele. "Near real-time detection and approximate location of pipe bursts and other events in water distribution systems." Thesis, University of Exeter, 2012. http://hdl.handle.net/10871/9862.

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The research work presented in this thesis describes the development and testing of a new data analysis methodology for the automated near real-time detection and approximate location of pipe bursts and other events which induce similar abnormal pressure/flow variations (e.g., unauthorised consumptions, equipment failures, etc.) in Water Distribution Systems (WDSs). This methodology makes synergistic use of several self-learning Artificial Intelligence (AI) and statistical/geostatistical techniques for the analysis of the stream of data (i.e., signals) collected and communicated on-line by the

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Junuthula, Ruthwik Reddy. "Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249.

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Cabeli, Vincent. "Learning causal graphs from continuous or mixed datasets of biological or clinical interest." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS331.

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Les travaux de cette thèse s’inscrivent dans la théorie principalement développée par Judea Pearl sur les diagrammes causaux; des modèles graphiques qui permettent de dériver toutes les quantités causales d’intérêt formellement et intuitivement. Nous traitons le problème de l’inférence de réseau causal à partir uniquement de données d’observation, c’est-à-dire sans aucune intervention de la part de l’expérimentateur. En particulier, nous proposons d’améliorer les méthodes existantes pour les rendre plus aptes à analyser des données issues du monde réel, en nous affranchissant le plus possible

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Vigraham, Saranyan A. "An Analog Evolvable Hardware Device for Active Control." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1195506953.

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Tugui, Catalin Adrian. "Design Methodology for High-performance Circuits Based on Automatic Optimization Methods." Thesis, Supélec, 2013. http://www.theses.fr/2013SUPL0002/document.

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Ce travail de thèse porte sur le développement d’une méthodologie efficace pour la conception analogique, des algorithmes et des outils correspondants qui peuvent être utilisés dans la conception dynamique de fonctions linéaires à temps continu. L’objectif principal est d’assurer que les performances pour un système complet peuvent être rapidement investiguées, mais avec une précision comparable aux évaluations au niveau transistor.Une première direction de recherche a impliqué le développement de la méthodologie de conception basée sur le processus d'optimisation automatique de cellules au ni

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FREIRE, Arthur Silva. "Modelo de redes bayesianas para melhoria do trabalho em equipe em projetos ágeis de desenvolvimento de software." Universidade Federal de Campina Grande, 2016. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/766.

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Submitted by Maria Medeiros (maria.dilva1@ufcg.edu.br) on 2018-05-22T12:37:25Z No. of bitstreams: 1 ARTHUR SILVA FREIRE -DISSERTAÇÃO (PPGCC) 2016.pdf: 2232664 bytes, checksum: 7d856251235ae5bacc2b971e556d50e3 (MD5)<br>Made available in DSpace on 2018-05-22T12:37:25Z (GMT). No. of bitstreams: 1 ARTHUR SILVA FREIRE -DISSERTAÇÃO (PPGCC) 2016.pdf: 2232664 bytes, checksum: 7d856251235ae5bacc2b971e556d50e3 (MD5) Previous issue date: 2016<br>Capes<br>A utilização de métodos ágeis requer que os indivíduos e as interações entre eles sejam considerados mais importantes que processos e ferramentas. Alé

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Virbalas, Linas. "Informacinių technologijų rizikos valdymo sistema." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2009. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2008~D_20090908_201756-32001.

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Šiuo darbu pristatoma sukurta sistema, kuria galima modeliuoti ir valdyti rizikas, kylančias iš IT, susijusias su IS nepasiekiamumu ar lėtu veikimu. Sistema realizuota pasitelkus neuroninius tinklus ir yra apmokoma sukaupta statistine informacija iš informacinių sistemų. Jai nurodoma, kurios statistinės informacijos laiko eilutes norima modeliuoti – t.y. kurios iš jų yra rizikos išraiška (serverių apkrovimas, IS atsakymo laikas ir pan.). Sistema pati nustato koreliuojančias statistines laiko eilutes, sugrupuoja susijusias ir kiekvienai grupei sukuria po modelį – apibendrina iki tol nežinomą pr

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Harlé, Flore. "Détection de ruptures multiples dans des séries temporelles multivariées : application à l'inférence de réseaux de dépendance." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT043/document.

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Cette thèse présente une méthode pour la détection hors-ligne de multiples ruptures dans des séries temporelles multivariées, et propose d'en exploiter les résultats pour estimer les relations de dépendance entre les variables du système. L'originalité du modèle, dit du Bernoulli Detector, réside dans la combinaison de statistiques locales issues d'un test robuste, comparant les rangs des observations, avec une approche bayésienne. Ce modèle non paramétrique ne requiert pas d'hypothèse forte sur les distributions des données. Il est applicable sans ajustement à la loi gaussienne comme sur des

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Jebreen, Kamel. "Modèles graphiques pour la classification et les séries temporelles." Thesis, Aix-Marseille, 2017. http://www.theses.fr/2017AIXM0248/document.

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Dans cette thèse nous nous intéressons aux méthodes de classifications supervisées utilisant les réseaux bayésiens. L'avantage majeur de ces méthodes est qu'elles peuvent prendre en compte les interactions entre les variables explicatives. Dans une première partie nous proposons une procédure de discrétisation spécifique et une procédure de sélection de variables qui permettent d'améliorer considérablement les classifieurs basés sur des réseaux bayésiens. Cette procédure a montré de très bonnes performances empiriques sur un grand choix de jeux de données connus de l’entrepôt d'apprentissage a

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Rahier, Thibaud. "Réseaux Bayésiens pour fusion de données statiques et temporelles." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM083/document.

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La prédiction et l'inférence sur des données temporelles sont très souvent effectuées en utilisant uniquement les séries temporelles. Nous sommes convaincus que ces tâches pourraient tirer parti de l'utilisation des métadonnées contextuelles associées aux séries temporelles, telles que l'emplacement, le type, etc. Réciproquement, les tâches de prédiction et d'inférence sur les métadonnées pourraient bénéficier des informations contenues dans les séries temporelles. Cependant, il n'existe pas de méthode standard pour modéliser conjointement les données de séries temporelles et les métadonnées d

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Kindermann, Lars. "Neuronale Netze zur Berechnung Iterativer Wurzeln und Fraktionaler Iterationen." Doctoral thesis, Universitätsbibliothek Chemnitz, 2002. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200201544.

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Diese Arbeit entwickelt eine Methode, Funktionalgleichungen der Art g(g(x))=f(x) bzw. g^n(x)=f(x) mit Hilfe neuronaler Netze zu lösen. Gesucht ist eine Funktion g(x), die mehrfach hintereinandergeschaltet genau einer gegebenen Funktion f(x) entspricht. Man nennt g=f^1/n eine iterative Wurzel oder fraktionale Iteration von f. Lösungen für g zu finden, stellt das inverse Problem der Iteration dar oder die Erweiterung der Wurzel- bzw. Potenzoperation auf die Funktionsalgebra. Geschlossene Ausdrücke für Funktionswurzeln einer gegebenen Funktion zu finden, ist in der Regel nicht möglich oder sehr s

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Tagscherer, Michael. "Dynamische Neuronale Netzarchitektur für Kontinuierliches Lernen." Doctoral thesis, Universitätsbibliothek Chemnitz, 2001. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200100725.

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Am Beispiel moderner Automatisierungssysteme wird deutlich, dass die Steuerung und optimale Führung der technischen Prozesse eng verbunden ist mit der Verfügbarkeit eines möglichst exakten Prozessmodells. Steht jedoch kein Modell des zu steuernden Systems zur Verfügung oder ist das System nicht ausreichend genau analytisch beschreibbar, muss ein adäquates Modell auf der Basis von Beobachtungen (Messdaten) abgeleitet werden. Erschwerend wirken sich hierbei starke Nichtlinearitäten sowie der zeitvariante Charakter der zu identifizierenden Systeme aus. Die Zeitvarianz, beispielsweise durch Alteru

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Kindermann, Lars. "Neuronale Netze zur Berechnung Iterativer Wurzeln und Fraktionaler Iterationen." [S.l. : s.n.], 2001. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10424174.

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LIU, ZHEN-ZHONG, and 劉振中. "Adaptively controlling nonlinear continuous-time systems using neural networks." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/71989183416198206106.

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Chien, Chia-Yi. "Construction of Continuous-State Bayesian Networks Using D-Separation Property and Partial Correlations." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2607200613535800.

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Chien, Chia-Yi, and 簡佳怡. "Construction of Continuous-State Bayesian Networks Using D-Separation Property and Partial Correlations." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/71625418714253282137.

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碩士<br>國立臺灣大學<br>工業工程學研究所<br>94<br>The development of microarray technology is capable of generating a huge amount of gene expression data at once to help us analyze the whole genome mechanism. Many analysis methods have been developed and applied to analyze the microarray data, such as Clustering analysis, Factor analysis and Bayesian networks. Bayesian networks can better help biologists to understand the biological meanings behind the microarray data. In general, algorithms of Bayesian network construction can be divided into two categories: the search-and-score approach and the constraint-b

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Walker, James. "Bayesian Inference and Model Selection for Partially-Observed, Continuous-Time, Stochastic Epidemic Models." Thesis, 2019. http://hdl.handle.net/2440/124703.

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Emerging infectious diseases are an ongoing threat to the health of populations around the world. In response, countries such as the USA, UK and Australia, have outlined data collection protocols to surveil these novel diseases. One of the aims of these data collection protocols is to characterise the disease in terms of transmissibility and clinical severity in order to inform an appropriate public health response. This kind of data collection protocol is yet to be enacted in Australia, but such a protocol is likely to be tested during a seasonal in uenza ( u) outbreak in the next few years.

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BARONE, ROSARIO. "MCMC methods for continuous time multi-state models and high dimensional copula models." Doctoral thesis, 2020. http://hdl.handle.net/11573/1365737.

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In this Thesis we propose Markov chain Monte Carlo (MCMC) methods for several classes of models. We consider both parametric and nonparametric Bayesian approaches proposing either alternatives in computation to already existent methods or new computational tools. In particular, we consider continuous time multi-state models (CTMSM), that is a class of stochastic processes useful for modelling several phenomena evolving continuously in time, with a finite number of states. Inference for these models is straightforward if the processes are fully observed, while it presents some computational d

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