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Relevant bibliographies by topics / Bayesian neural network / Dissertations / Theses

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Dissertations / Theses on the topic 'Bayesian neural network'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Wong, Helen. "A Bayesian neural network for censored survival data." Thesis, Liverpool John Moores University, 2001. http://researchonline.ljmu.ac.uk/4918/.

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2

Bate, Andrew. "The use of Bayesian confidence propagation neural network in pharmacovigilance." Doctoral thesis, Umeå University, Pharmacology and Clinical Neuroscience, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-83.

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<p>The WHO database contains more than 2.8 million case reports of suspected adverse drug reactions reported from 70 countries worldwide since 1968. The Uppsala Monitoring Centre maintains and analyses this database for new signals on behalf of the WHO Programme for International Drug Monitoring. A goal of the Programme is to detect signals, where a signal is defined as "Reported information on a possible causal relationship between an adverse event and a drug, the relationship being unknown or incompletely documented previously."</p><p>The analysis of such a large amount of data on a case by

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3

Ericson, Julia. "Modelling Immediate Serial Recall using a Bayesian Attractor Neural Network." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-291553.

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In the last decades, computational models have become useful tools for studying biological neural networks. These models are typically constrained by either behavioural data from neuropsychological studies or by biological data from neuroscience. One model of the latter kind is the Bayesian Confidence Propagating Neural Network (BCPNN) - an attractor network with a Bayesian learning rule which has been proposed as a model for various types of memory. In this thesis, I have further studied the potential of the BCPNN in short-term sequential memory. More specifically, I have investigated if the

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4

Woxén, Gustav. "Predicting Hospital Attendance with Neural Networks and Bayesian Inference." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-174556.

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Missed hospital appointments is a globally acknowledged problem. In order to minimize the cost associated with this, attempts have been made to predict what appointments will be missed using statistical models and machine learning. However, in all previous models, information about a patient’s previous appointments has been ignored to some extent. In this thesis, a novel way of incorporating previous appointment data in more detail is proposed. This is done by firstly estimating a prior attendance probability based on general data using an artificial neural network, and then updating it using

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5

PEREGO, RICCARDO. "Automated Deep Learning through Constrained Bayesian Optimization." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/314922.

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In un mondo sempre più tecnologico e interconnesso, la quantità di dati è in continua crescita e, di conseguenza, anche gli algoritmi di decision-making sono in continua evoluzione per adattarsi ad essi. Una delle principali fonti di questa grande quantità di dati è l'Internet of Things, in cui miliardi di sensori si scambiano informazioni attraverso la rete per svolgere vari tipi di attività come il monitoraggio industriale e medico. Negli ultimi anni lo sviluppo tecnologico ha permesso di definire nuove architetture hardware ad alte prestazioni per i sensori, detti microcontrollori, che hann

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6

Donaldson, Soberanis Ivy Elizabeth. "An extended Bayesian network approach for analyzing supply chain disruptions." Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/489.

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Supply chain management (SCM) is the oversight of materials, information, and finances as they move in a process from supplier to manufacturer to wholesaler to retailer to consumer. Supply chain management involves coordinating and integrating these flows both within and among companies as efficiently as possible. The supply chain consists of interconnected components that can be complex and dynamic in nature. Therefore, an interruption in one subnetwork of the system may have an adverse effect on another subnetworks, which will result in a supply chain disruption. Disruptions from an event or

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7

Orre, Roland. "On Data Mining and Classification Using a Bayesian Confidence Propagation Neural Network." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3592.

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<p>The aim of this thesis is to describe how a statisticallybased neural network technology, here named BCPNN (BayesianConfidence Propagation Neural Network), which may be identifiedby rewriting Bayes' rule, can be used within a fewapplications, data mining and classification with credibilityintervals as well as unsupervised pattern recognition.</p><p>BCPNN is a neural network model somewhat reminding aboutBayesian decision trees which are often used within artificialintelligence systems. It has previously been success- fullyapplied to classification tasks such as fault diagnosis,supervised pa

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8

Likhari, Amitoj S. "Computational accounts of attentional bias neural network and Bayesian network models of the dot probe paradigm /." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0009481.

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9

Khan, Mohammad Sajjad Coulibaly Paulin. "Climate change impact study on water resources with uncertainty estimates using Bayesian neural network." *McMaster only, 2006.

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10

Pereira, Patrícia. "Attractor Neural Network modelling of the Lifespan Retrieval Curve." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280732.

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Human capability to recall episodic memories depends on how much time has passed since the memory was encoded. This dependency is described by a memory retrieval curve that reflects an interesting phenomenon referred to as a reminiscence bump - a tendency for older people to recall more memories formed during their young adulthood than in other periods of life. This phenomenon can be modelled with an attractor neural network, for example, the firing-rate Bayesian Confidence Propagation Neural Network (BCPNN) with incremental learning. In this work, the mechanisms underlying the reminiscence bu

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11

Ghimire, Manoj. "Switching Neural Network Systems for Nonlinear Tracking." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright154708422929052.

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12

Sharaf, Taysseer. "Statistical Learning with Artificial Neural Network Applied to Health and Environmental Data." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5866.

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The current study illustrates the utilization of artificial neural network in statistical methodology. More specifically in survival analysis and time series analysis, where both holds an important and wide use in many applications in our real life. We start our discussion by utilizing artificial neural network in survival analysis. In literature there exist two important methodology of utilizing artificial neural network in survival analysis based on discrete survival time method. We illustrate the idea of discrete survival time method and show how one can estimate the discrete model using ar

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13

Darcy, Peter. "Resolving RFID Anomalies using Intelligent Analysis and Classification." Thesis, Griffith University, 2012. http://hdl.handle.net/10072/366922.

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Radio Frequency Identication (RFID) technology refers to the use of tags with unique identiers being attached to various items which are scanned without a line of sight and then recorded into a database. Current integrations of this technology include baggage tracking at airports, pet owner identication and tagging objects in stores to enforce security by alerting management when an item has left the facility without the tag being deactivated. Despite the wide-scale adoption and advantages of RFID, several issues exist that introduce a level of unreliability resulting in the technology only be

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Tang, Adelina Lai Toh. "Application of the tree augmented naive Bayes network to classification and forecasting /." [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe.pdf.

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15

Lundh, Felix, and Oscar Barta. "Hyperparameters relationship to the test accuracy of a convolutional neural network." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19846.

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Machine learning for image classification is a hot topic and it is increasing in popularity. Therefore the aim of this study is to provide a better understanding of convolutional neural network hyperparameters by comparing the test accuracy of convolutional neural network models with different hyperparameter value configurations. The focus of this study is to see whether there is an influence in the learning process depending on which hyperparameter values were used. For conducting the experiments convolutional neural network models were developed using the programming language Python utilizin

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Shapero, Samuel Andre. "Configurable analog hardware for neuromorphic Bayesian inference and least-squares solutions." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/51719.

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Sparse approximation is a Bayesian inference program with a wide number of signal processing applications, such as Compressed Sensing recovery used in medical imaging. Previous sparse coding implementations relied on digital algorithms whose power consumption and performance scale poorly with problem size, rendering them unsuitable for portable applications, and a bottleneck in high speed applications. A novel analog architecture, implementing the Locally Competitive Algorithm (LCA), was designed and programmed onto a Field Programmable Analog Arrays (FPAAs), using floating gate transistors to

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Ayodeji, Akiwowo. "Developing integrated data fusion algorithms for a portable cargo screening detection system." Thesis, Loughborough University, 2012. https://dspace.lboro.ac.uk/2134/9901.

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Towards having a one size fits all solution to cocaine detection at borders; this thesis proposes a systematic cocaine detection methodology that can use raw data output from a fibre optic sensor to produce a set of unique features whose decisions can be combined to lead to reliable output. This multidisciplinary research makes use of real data sourced from cocaine analyte detecting fibre optic sensor developed by one of the collaborators - City University, London. This research advocates a two-step approach: For the first step, the raw sensor data are collected and stored. Level one fusion i.

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Koduri, Santhosh K. "Application of Bayesian Neural Network Modeling to Characterize the Interrelationship between Microstructure and Mechanical Property in Alpha+Beta-Titanium Alloys." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1275402649.

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Harborn, Jakob. "EVALUATING THE IMPACT OF UNCERTAINTY ON THE INTEGRITY OF DEEP NEURAL NETWORKS." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-53395.

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Deep Neural Networks (DNNs) have proven excellent performance and are very successful in image classification and object detection. Safety critical industries such as the automotive and aerospace industry aim to develop autonomous vehicles with the help of DNNs. In order to certify the usage of DNNs in safety critical systems, it is essential to prove the correctness of data within the system. In this thesis, the research is focused on investigating the sources of uncertainty, what effects various sources of uncertainty has on NNs, and how it is possible to reduce uncertainty within an NN. Pro

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Tully, Philip. "Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits." Doctoral thesis, KTH, Beräkningsvetenskap och beräkningsteknik (CST), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205568.

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Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. What dynamical phenomena exist to act together to balance such learning with information processing? What types of activity patterns do they underpin, and how do these patterns relate to our perceptual experiences? What enables learning and memory operations to occur despite such massive and cons

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Foltz, John Wendell IV. "The Relationships Between Microstructure, Tensile Properties and Fatigue Life in Ti-5Al-5V-5Mo-3Cr-0.4Fe (Ti-5553)." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1286207330.

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PONSI, FEDERICO. "Tecniche di model updating e soft computing per l'identificazione dei parametri meccanici e del danno di strutture." Doctoral thesis, Università degli studi di Modena e Reggio Emilia, 2022. http://hdl.handle.net/11380/1277127.

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Numerose strutture e infrastrutture si trovano in uno stato di degrado o danneggiamento causato, per esempio, da un evento sismico o dall’esposizione prolungata a condizioni ambientali sfavorevoli. Questa situazione può compromettere la funzionalità dell’opera o, in casi più gravi, portare al collasso della stessa. In questo contesto, il monitoraggio dello stato di salute delle strutture mediante prove dinamiche rappresenta un’attività in crescente diffusione. Le metodologie per l’identificazione del danno basate sui dati acquisti dal sistema di monitoraggio sono spesso supportate dalla defini

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23

Posani, Lorenzo. "Inference and modeling of biological networks : a statistical-physics approach to neural attractors and protein fitness landscapes." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE043/document.

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L'avènement récent des procédures expérimentales à haut débit a ouvert une nouvelle ère pour l'étude quantitative des systèmes biologiques. De nos jours, les enregistrements d'électrophysiologie et l'imagerie du calcium permettent l'enregistrement simultané in vivo de centaines à des milliers de neurones. Parallèlement, grâce à des procédures de séquençage automatisées, les bibliothèques de protéines fonctionnelles connues ont été étendues de milliers à des millions en quelques années seulement. L'abondance actuelle de données biologiques ouvre une nouvelle série de défis aux théoriciens. Des

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Du, Toit Jan Valentine. "Automated construction of generalized additive neural networks for predictive data mining / Jan Valentine du Toit." Thesis, North-West University, 2006. http://hdl.handle.net/10394/128.

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In this thesis Generalized Additive Neural Networks (GANNs) are studied in the context of predictive Data Mining. A GANN is a novel neural network implementation of a Generalized Additive Model. Originally GANNs were constructed interactively by considering partial residual plots. This methodology involves subjective human judgment, is time consuming, and can result in suboptimal results. The newly developed automated construction algorithm solves these difficulties by performing model selection based on an objective model selection criterion. Partial residual plots are only utilized after the

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Comuni, Federica. "A natural language processing solution to probable Alzheimer’s disease detection in conversation transcripts." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-19889.

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This study proposes an accuracy comparison of two of the best performing machine learning algorithms in natural language processing, the Bayesian Network and the Long Short-Term Memory (LSTM) Recurrent Neural Network, in detecting Alzheimer’s disease symptoms in conversation transcripts. Because of the current global rise of life expectancy, the number of seniors affected by Alzheimer’s disease worldwide is increasing each year. Early detection is important to ensure that affected seniors take measures to relieve symptoms when possible or prepare plans before further cognitive decline occurs.

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GIOVANNELLI, ALESSANDRO. "Nonlinear forecasting using a large number of predictors." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2010. http://hdl.handle.net/2108/1333.

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L’obiettivo principale di questa tesi è di introdurre un modello non lineare, “Feedforward Neural Network-Dynamic Factor” (FNN-DF), per la previsione di serie macroeconomiche utilizzando un numero elevato di variabili. La tecnica usata per riassumere le variabili in un piccolo numero di fattori è il “Generalized Dynamic Factor Model” (GDFM), mentre le reti neurali di tipo “Feedforward” sono utilizzate per rappresentare la non-linearità. Comunemente nella letteratura del GDFM, le previsioni sono effettuate con modelli lineari. Tuttavia tali tecniche spesso non sono correttamente specificate e l

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Koudelka, Vlastimil. "Pravděpodobnostní neuronové sítě pro speciální úlohy v elektromagnetismu." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-233661.

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Tato práce pojednává o technikách behaviorálního modelování pro speciální úlohy v elektromagnetismu, které je možno formulovat jako problém aproximace, klasifikace, odhadu hustoty pravděpodobnosti nebo kombinatorické optimalizace. Zkoumané methody se dotýkají dvou základních problémů ze strojového učení a combinatorické optimalizace: ”bias vs. variance dilema” a NP výpočetní komplexity. Boltzmanův stroj je v práci navržen ke zjednodušování komplexních impedančních sítí. Bayesovský přístup ke strojovému učení je upraven pro regularizaci Parzenova okna se snahou o vytvoření obecného kritéria pro

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Gadi, Manoel Fernando Alonso. "Uma comparação de métodos de classificação aplicados à detecção de fraude em cartões de crédito." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/45/45134/tde-11062008-161212/.

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Em anos recentes, muitos algoritmos bio-inspirados têm surgido para resolver problemas de classificação. Em confirmação a isso, a revista Nature, em 2002, publicou um artigo que já apontava para o ano de 2003 o uso comercial de Sistemas Imunológicos Artificiais para detecção de fraude em instituições financeiras por uma empresa britânica. Apesar disso, não observamos, a luz de nosso conhecimento, nenhuma publicação científica com resultados promissores desde então. Nosso trabalho tratou de aplicar Sistemas Imunológicos Artificiais (AIS) para detecção de fraude em cartões de crédito. Comparamos

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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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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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Monjoly, Stéphanie. "Outils de prédiction pour la production d’électricité d’origine éolienne : application à l’optimisation du couplage aux réseaux de distributions d’électricité." Thesis, Antilles-Guyane, 2013. http://www.theses.fr/2013AGUY0679/document.

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La forte variabilité de la vitesse du vent fait que l'énergie produite par un parc éolien n'est pas constante dans le temps. Le gestionnaire ne peut donc pas dimensionner son réseau électrique en prenant intégralement ce type de production en compte. L' une des solutions préconisées pour permettre le développement de l' éolien et son intégration avec une plus grande sureté aux réseaux, est de développer et d'améliorer les outils de prévisions. Le travail de thèse consiste à améliorer les performances d'un outil de prédiction basé sur les réseaux de neurones bayesiens, permettant la prédiction

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PETRINI, ALESSANDRO. "HIGH PERFORMANCE COMPUTING MACHINE LEARNING METHODS FOR PRECISION MEDICINE." Doctoral thesis, Università degli Studi di Milano, 2021. http://hdl.handle.net/2434/817104.

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La Medicina di Precisione (Precision Medicine) è un nuovo paradigma che sta rivoluzionando diversi aspetti delle pratiche cliniche: nella prevenzione e diagnosi, essa è caratterizzata da un approccio diverso dal "one size fits all" proprio della medicina classica. Lo scopo delle Medicina di Precisione è di trovare misure di prevenzione, diagnosi e cura che siano specifiche per ciascun individuo, a partire dalla sua storia personale, stile di vita e fattori genetici. Tre fattori hanno contribuito al rapido sviluppo della Medicina di Precisione: la possibilità di generare rapidamente ed econo

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Tipler, Steven. "Characterization of a light petroleum fraction produced from automotive shredder residues." Doctoral thesis, Universite Libre de Bruxelles, 2021. https://dipot.ulb.ac.be/dspace/bitstream/2013/323435/5/contratST.pdf.

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Wastes have a real potential as being players in the energy mix of tomorrow. They can have a high heating value depending on their composition, which makes them good candidates to be converted into liquid fuel via pyrolysis. Among the different types of wastes, automotive residues are expected to rocket due to the increasing number of cars and the tendency to build cars with more and more polymers. Moreover, the existing regulations concerning the recycling of end-of-life vehicles become more and more stringent. Unconventional fuels such as those derived from automotive shredder residues (ASR)

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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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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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Rodrigo, Hansapani Sarasepa. "Bayesian Artificial Neural Networks in Health and Cybersecurity." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6940.

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Being in the era of Big data, the applicability and importance of data-driven models like artificial neural network (ANN) in the modern statistics have increased substantially. In this dissertation, our main goal is to contribute to the development and the expansion of these ANN models by incorporating Bayesian learning techniques. We have demonstrated the applicability of these Bayesian ANN models in interdisciplinary research including health and cybersecurity. Breast cancer is one of the leading causes of deaths among females. Early and accurate diagnosis is a critic

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Graff, Philip B. "Bayesian methods for gravitational waves and neural networks." Thesis, University of Cambridge, 2012. https://www.repository.cam.ac.uk/handle/1810/244270.

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Einstein’s general theory of relativity has withstood 100 years of testing and will soon be facing one of its toughest challenges. In a few years we expect to be entering the era of the first direct observations of gravitational waves. These are tiny perturbations of space-time that are generated by accelerating matter and affect the measured distances between two points. Observations of these using the laser interferometers, which are the most sensitive length-measuring devices in the world, will allow us to test models of interactions in the strong field regime of gravity and eventually gene

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Magni, Alessandro Rodolfo. "Bayesian methods for neural networks in process identification." Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343899.

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SARCIA', SALVATORE ALESSANDRO. "An Approach to improving parametric estimation models in the case of violation of assumptions based upon risk analysis." Doctoral thesis, Università degli Studi di Roma "Tor Vergata", 2009. http://hdl.handle.net/2108/1048.

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In this work, we show the mathematical reasons why parametric models fall short of providing correct estimates and define an approach that overcomes the causes of these shortfalls. The approach aims at improving parametric estimation models when any regression model assumption is violated for the data being analyzed. Violations can be that, the errors are x-correlated, the model is not linear, the sample is heteroscedastic, or the error probability distribution is not Gaussian. If data violates the regression assumptions and we do not deal with the consequences of these violations, we cannot i

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Stassopoulou, Athena. "Bayesian networks for inference with geographic information systems." Thesis, University of Surrey, 1996. http://epubs.surrey.ac.uk/863/.

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Saad, Ali. "Detection of Freezing of Gait in Parkinson's disease." Thesis, Le Havre, 2016. http://www.theses.fr/2016LEHA0029/document.

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Le risque de chute provoqué par le phénomène épisodique de ‘Freeze of Gait’ (FoG) est un symptôme commun de la maladie de Parkinson. Cette étude concerne la détection et le diagnostic des épisodes de FoG à l'aide d'un prototype multi-capteurs. La première contribution est l'introduction de nouveaux capteurs (télémètres et goniomètres) dans le dispositif de mesure pour la détection des épisodes de FoG. Nous montrons que l'information supplémentaire obtenue avec ces capteurs améliore les performances de la détection. La seconde contribution met œuvre un algorithme de détection basé sur des résea

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Nortje, W. D. "Comparison of Bayesian learning and conjugate gradient descent training of neural networks." Diss., University of Pretoria, 2001. http://hdl.handle.net/2263/29327.

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Neural networks are used in various fields to make predictions about the future value of a time series, or about the class membership of a given object. For the network to be effective, it needs to be trained on a set of training data combined with the expected results. Two aspects to keep in mind when considering a neural network as a solution, are the required training time and the prediction accuracy. This research compares the classification accuracy of conjugate gradient descent neural networks and Bayesian learning neural networks. Conjugate gradient descent networks are known for their

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Alfonso, Pérez Gerardo. "Bayesian neural networks to predict aging and disease risk." Doctoral thesis, Universitat Autònoma de Barcelona, 2019. http://hdl.handle.net/10803/669851.

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Ha habido un mayor enfoque en la medicina personalizada en los últimos años. Las mejoras tecnológicas significativas en las últimas décadas han generando una explosión en los datos disponibles y esto ha sido uno de los impulsores de la expansión de la medicina personalizada. Por ejemplo, la cantidad de datos de metilación del ADN, así como los datos SNP disponibles, ha aumentado considerablemente. Esta tesis se centra en las técnicas de análisis de estos datos aplicados al campo del envejecimiento, así como a la detección de enfermedades, más concretamente para la identificación del cáncer y l

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Mbuvha, Rendani. "Bayesian Neural Networks for Short Term Wind Power Forecasting." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210725.

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In recent years, wind and other variable renewable energy sources have gained a rapidly increasing share of the global energy mix. In this context the greatest concern facing renewable energy sources like wind is the uncertainty in production volumes as their generation ability is inherently dependent on weather conditions. When providing forecasts for newly commissioned wind farms there is a limited amount of historical power production data, while the number of potential features from different weather forecast providers is vast. Bayesian regularization is therefore seen as a possible techni

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Li, Junning. "Dynamic Bayesian networks : modeling and analysis of neural signals." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/12618.

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Studying interactions between different brain regions or neural components is crucial in understanding neurological disorders. Dynamic Bayesian networks, a type of statistical graphical model, have been suggested as a promising tool to model neural communication systems. This thesis investigates the employment of dynamic Bayesian networks for analyzing neural connectivity, especially with focus on three topics: structural feature extraction, group analysis, and error control in learning network structures. Extracting interpretable features from experimental data is important for clinical diag

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Candido, Jorge. "Detecção e rastreio de faces utilizando redes Bayesianas." Universidade Presbiteriana Mackenzie, 2007. http://tede.mackenzie.br/jspui/handle/tede/1451.

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Made available in DSpace on 2016-03-15T19:37:53Z (GMT). No. of bitstreams: 1 Jorge Candido.pdf: 1397085 bytes, checksum: e82ad6a587c16813c068018b87471755 (MD5) Previous issue date: 2007-02-26<br>This work presents a face detection system that uses a Bayesian Network to combine information from different computational cheap visual operators. The aim in this work is to show that combining simple features in a Bayesian Network allows building an enhanced face detector system, increasing the detection rate and speeding up the face detection process. This face detector has been developed to work

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Kalkandara, Karolina. "Neural networks and classification trees for misclassified data." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.312187.

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Ramachandran, Sowmya. "Theory refinement of Bayesian networks with hidden variables /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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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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Martínez-García, Marina. "Statistical analysis of neural correlates in decision-making." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/283111.

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We investigated the neuronal processes which occur during a decision- making task based on a perceptual classi cation judgment. For this purpose we have analysed three di erent experimental paradigms (somatosensory, visual, and auditory) in two di erent species (monkey and rat), with the common goal of shedding light into the information carried by neurons. In particular, we focused on how the information content is preserved in the underlying neuronal activity over time. Furthermore we considered how the decision, the stimuli, and the con dence are encoded in memory and, when the exp

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