Дисертації з теми "Multiple Sparse Bayesian Learning"
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Higson, Edward John. "Bayesian methods and machine learning in astrophysics." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289728.
Повний текст джерелаParisi, Simone [Verfasser], Jan [Akademischer Betreuer] Peters, and Joschka [Akademischer Betreuer] Boedeker. "Reinforcement Learning with Sparse and Multiple Rewards / Simone Parisi ; Jan Peters, Joschka Boedeker." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2020. http://d-nb.info/1203301545/34.
Повний текст джерелаTandon, Prateek. "Bayesian Aggregation of Evidence for Detection and Characterization of Patterns in Multiple Noisy Observations." Research Showcase @ CMU, 2015. http://repository.cmu.edu/dissertations/658.
Повний текст джерелаTiclavilca, Andres M. "Multivariate Bayesian Machine Learning Regression for Operation and Management of Multiple Reservoir, Irrigation Canal, and River Systems." DigitalCommons@USU, 2010. https://digitalcommons.usu.edu/etd/600.
Повний текст джерелаJin, Junyang. "Novel methods for biological network inference : an application to circadian Ca2+ signaling network." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/285323.
Повний текст джерелаYazdani, Akram. "Statistical Approaches in Genome-Wide Association Studies." Doctoral thesis, Università degli studi di Padova, 2014. http://hdl.handle.net/11577/3423743.
Повний текст джерелаLo Studio di Associazione Genome-Wide, GWAS, tipicamente comprende centinaia di migliaia di polimorfismi a singolo nucleotide, SNPs, genotipizzati per pochi campioni. L'obiettivo di tale studio consiste nell'individuare le regioni cruciali SNPs e prevedere gli esiti di una variabile risposta. Dal momento che il numero di predittori è di gran lunga superiore al numero di campioni, non è possibile condurre l'analisi dei dati con metodi statistici classici. GWAS attuali, i metodi negli maggiormente utilizzati si basano sull'analisi a marcatore unico, che valuta indipendentemente l'associazione di ogni SNP con i tratti complessi. A causa della bassa potenza dell'analisi a marcatore unico nel rilevamento delle associazioni reali, l'analisi simultanea ha recentemente ottenuto più attenzione. I recenti metodi per l'analisi simultanea nel multidimensionale hanno una limitazione sulla disparità tra il numero di predittori e il numero di campioni. Pertanto, è necessario ridurre la dimensionalità dell'insieme di SNPs. Questa tesi fornisce una panoramica dell'analisi a marcatore singolo e dell'analisi simultanea, focalizzandosi su metodi Bayesiani. Vengono discussi i limiti di tali approcci in relazione ai GWAS, con riferimento alla letteratura recente e utilizzando studi di simulazione. Per superare tali problemi, si è cercato di ridurre la dimensione dell'insieme di SNPs con una tecnica a proiezione casuale. Poiché questo approccio non comporta miglioramenti nella accuratezza predittiva del modello, viene quindi proposto un approccio in due fasi, che risulta essere un metodo ibrido di analisi singola e simultanea. Tale approccio, completamente Bayesiano, seleziona gli SNPs più promettenti nella prima fase valutando l'impatto di ogni marcatore indipendentemente. Nella seconda fase, viene sviluppato un modello gerarchico Bayesiano per analizzare contemporaneamente l'impatto degli indicatori selezionati. Il modello che considera i campioni correlati pone una priori locale-globale ristretta sugli effetti dei marcatori. Tale prior riduce a zero gli effetti piccoli, mentre mantiene gli effetti più grandi relativamente grandi. Le priori specificate sugli effetti dei marcatori sono rappresentazioni gerarchiche della distribuzione Pareto doppia; queste a priori migliorano le prestazioni predittive del modello. Infine, nella tesi vengono riportati i risultati dell'analisi su dati reali di SNP basate sullo studio a marcatore singolo e sul nuovo approccio a due stadi.
Deshpande, Hrishikesh. "Dictionary learning for pattern classification in medical imaging." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S032/document.
Повний текст джерелаMost natural signals can be approximated by a linear combination of a few atoms in a dictionary. Such sparse representations of signals and dictionary learning (DL) methods have received a special attention over the past few years. While standard DL approaches are effective in applications such as image denoising or compression, several discriminative DL methods have been proposed to achieve better image classification. In this thesis, we have shown that the dictionary size for each class is an important factor in the pattern recognition applications where there exist variability difference between classes, in the case of both the standard and discriminative DL methods. We validated the proposition of using different dictionary size based on complexity of the class data in a computer vision application such as lips detection in face images, followed by more complex medical imaging application such as classification of multiple sclerosis (MS) lesions using MR images. The class specific dictionaries are learned for the lesions and individual healthy brain tissues, and the size of the dictionary for each class is adapted according to the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients
Chen, Cong. "High-Dimensional Generative Models for 3D Perception." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103948.
Повний текст джерелаDoctor of Philosophy
The development of automation systems and robotics brought the modern world unrivaled affluence and convenience. However, the current automated tasks are mainly simple repetitive motions. Tasks that require more artificial capability with advanced visual cognition are still an unsolved problem for automation. Many of the high-level cognition-based tasks require the accurate visual perception of the environment and dynamic objects from the data received from the optical sensor. The capability to represent, identify and interpret complex visual data for understanding the geometric structure of the world is 3D perception. To better tackle the existing 3D perception challenges, this dissertation proposed a set of generative learning-based frameworks on sparse tensor data for various high-dimensional robotics perception applications: underwater point cloud filtering, image restoration, deformation detection, and localization. Underwater point cloud data is relevant for many applications such as environmental monitoring or geological exploration. The data collected with sonar sensors are however subjected to different types of noise, including holes, noise measurements, and outliers. In the first chapter, we propose a generative model for point cloud data recovery using Variational Bayesian (VB) based sparse tensor factorization methods to tackle these three defects simultaneously. In the second part of the dissertation, we propose an image restoration technique to tackle missing data, which is essential for many perception applications. An efficient generative chaotic RNN framework has been introduced for recovering the sparse tensor from a single corrupted image for various types of missing data. In the last chapter, a multi-level CNN for high-dimension tensor feature extraction for underwater vehicle localization has been proposed.
Subramanian, Harshavardhan. "Combining scientific computing and machine learning techniques to model longitudinal outcomes in clinical trials." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176427.
Повний текст джерелаFrancisco, André Biasin Segalla. "Esparsidade estruturada em reconstrução de fontes de EEG." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/43/43134/tde-13052018-112615/.
Повний текст джерелаFunctional Neuroimaging is an area of neuroscience which aims at developing several techniques to map the activity of the nervous system and has been under constant development in the last decades due to its high importance in clinical applications and research. Common applied techniques such as functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have great spatial resolution (~ mm), but a limited temporal resolution (~ s), which poses a great challenge on our understanding of the dynamics of higher cognitive functions, whose oscillations can occur in much finer temporal scales (~ ms). Such limitation occurs because these techniques rely on measurements of slow biological responses which are correlated in a complicated manner to the actual electric activity. The two major candidates that overcome this shortcoming are Electro- and Magnetoencephalography (EEG/MEG), which are non-invasive techniques that measure the electric and magnetic fields on the scalp, respectively, generated by the electrical brain sources. Both have millisecond temporal resolution, but typically low spatial resolution (~ cm) due to the highly ill-posed nature of the electromagnetic inverse problem. There has been a huge effort in the last decades to improve their spatial resolution by means of incorporating relevant information to the problem from either other imaging modalities and/or biologically inspired constraints allied with the development of sophisticated mathematical methods and algorithms. In this work we focus on EEG, although all techniques here presented can be equally applied to MEG because of their identical mathematical form. In particular, we explore sparsity as a useful mathematical constraint in a Bayesian framework called Sparse Bayesian Learning (SBL), which enables the achievement of meaningful unique solutions in the source reconstruction problem. Moreover, we investigate how to incorporate different structures as degrees of freedom into this framework, which is an application of structured sparsity and show that it is a promising way to improve the source reconstruction accuracy of electromagnetic imaging methods.
Zambonin, Giuliano. "Development of Machine Learning-based technologies for major appliances: soft sensing for drying technology applications." Doctoral thesis, Università degli studi di Padova, 2019. http://hdl.handle.net/11577/3425771.
Повний текст джерелаUmakanthan, Sabanadesan. "Human action recognition from video sequences." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/93749/1/Sabanadesan_Umakanthan_Thesis.pdf.
Повний текст джерелаAzevedo, 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.
Повний текст джерелаTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação
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
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, no entanto, devem ser tomadas sob informação incompleta, o que pode resultar em ações precipitadas com consequências imprevisíveis. Quando uma solução deve ser selecionada sob vários pontos de vista conflitantes para operar em ambientes ruidosos e variantes no tempo, implementar alternativas provisórias flexíveis pode ser fundamental para contornar a falta de informação completa, mantendo opções futuras em aberto. A engenharia antecipatória pode então ser considerada como a estratégia de conceber soluções flexíveis as quais permitem aos tomadores de decisão responder de forma robusta a cenários imprevisíveis. Essa estratégia pode, assim, mitigar os riscos de, sem intenção, se comprometer fortemente a alternativas incertas, ao mesmo tempo em que aumenta a adaptabilidade às mudanças futuras. Nesta tese, os papéis da antecipação e da flexibilidade na automação de processos de tomada de decisão sequencial com múltiplos critérios sob incerteza é investigado. O dilema de atribuir importâncias relativas aos critérios de decisão e a recompensas imediatas sob informação incompleta é então tratado pela antecipação autônoma de decisões flexíveis capazes de preservar ao máximo a diversidade de escolhas futuras. Uma metodologia de aprendizagem antecipatória on-line é então proposta para melhorar a variedade e qualidade dos conjuntos futuros de soluções de trade-off. Esse objetivo é alcançado por meio da previsão de conjuntos de máximo hipervolume esperado, para a qual as capacidades de antecipação de metaheurísticas multi-objetivo são incrementadas com rastreamento bayesiano em ambos os espaços de busca e dos objetivos. A metodologia foi aplicada para a obtenção de decisões de investimento, as quais levaram a melhoras significativas do hipervolume futuro de conjuntos de carteiras financeiras de trade-off avaliadas com dados de ações fora da amostra de treino, quando comparada a uma estratégia míope. Além disso, a tomada de decisões flexíveis para o rebalanceamento de carteiras foi confirmada como uma estratégia significativamente melhor do que a de escolher aleatoriamente uma decisão de investimento a partir da fronteira estocástica eficiente evoluída, em todos os mercados artificiais e reais testados. Finalmente, os resultados sugerem que a antecipação de opções flexíveis levou a composições de carteiras que se mostraram significativamente correlacionadas com as melhorias observadas no hipervolume futuro esperado, avaliado com dados fora das amostras de treino
Abstract: The presence of uncertainty in future outcomes can lead to indecision in choice processes, especially when eliciting the relative importances of multiple decision criteria and of long-term vs. near-term performance. Some decisions, however, must be taken under incomplete information, what may result in precipitated actions with unforeseen consequences. When a solution must be selected under multiple conflicting views for operating in time-varying and noisy environments, implementing flexible provisional alternatives can be critical to circumvent the lack of complete information by keeping future options open. Anticipatory engineering can be then regarded as the strategy of designing flexible solutions that enable decision makers to respond robustly to unpredictable scenarios. This strategy can thus mitigate the risks of strong unintended commitments to uncertain alternatives, while increasing adaptability to future changes. In this thesis, the roles of anticipation and of flexibility on automating sequential multiple criteria decision-making processes under uncertainty are investigated. The dilemma of assigning relative importances to decision criteria and to immediate rewards under incomplete information is then handled by autonomously anticipating flexible decisions predicted to maximally preserve diversity of future choices. An online anticipatory learning methodology is then proposed for improving the range and quality of future trade-off solution sets. This goal is achieved by predicting maximal expected hypervolume sets, for which the anticipation capabilities of multi-objective metaheuristics are augmented with Bayesian tracking in both the objective and search spaces. The methodology has been applied for obtaining investment decisions that are shown to significantly improve the future hypervolume of trade-off financial portfolios for out-of-sample stock data, when compared to a myopic strategy. Moreover, implementing flexible portfolio rebalancing decisions was confirmed as a significantly better strategy than to randomly choosing an investment decision from the evolved stochastic efficient frontier in all tested artificial and real-world markets. Finally, the results suggest that anticipating flexible choices has lead to portfolio compositions that are significantly correlated with the observed improvements in out-of-sample future expected hypervolume
Doutorado
Engenharia de Computação
Doutor em Engenharia Elétrica
Cherief-Abdellatif, Badr-Eddine. "Contributions to the theoretical study of variational inference and robustness." Electronic Thesis or Diss., Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAG001.
Повний текст джерелаThis PhD thesis deals with variational inference and robustness. More precisely, it focuses on the statistical properties of variational approximations and the design of efficient algorithms for computing them in an online fashion, and investigates Maximum Mean Discrepancy based estimators as learning rules that are robust to model misspecification.In recent years, variational inference has been extensively studied from the computational viewpoint, but only little attention has been put in the literature towards theoretical properties of variational approximations until very recently. In this thesis, we investigate the consistency of variational approximations in various statistical models and the conditions that ensure the consistency of variational approximations. In particular, we tackle the special case of mixture models and deep neural networks. We also justify in theory the use of the ELBO maximization strategy, a model selection criterion that is widely used in the Variational Bayes community and is known to work well in practice.Moreover, Bayesian inference provides an attractive online-learning framework to analyze sequential data, and offers generalization guarantees which hold even under model mismatch and with adversaries. Unfortunately, exact Bayesian inference is rarely feasible in practice and approximation methods are usually employed, but do such methods preserve the generalization properties of Bayesian inference? In this thesis, we show that this is indeed the case for some variational inference algorithms. We propose new online, tempered variational algorithms and derive their generalization bounds. Our theoretical result relies on the convexity of the variational objective, but we argue that our result should hold more generally and present empirical evidence in support of this. Our work presents theoretical justifications in favor of online algorithms that rely on approximate Bayesian methods. Another point that is addressed in this thesis is the design of a universal estimation procedure. This question is of major interest, in particular because it leads to robust estimators, a very hot topic in statistics and machine learning. We tackle the problem of universal estimation using a minimum distance estimator based on the Maximum Mean Discrepancy. We show that the estimator is robust to both dependence and to the presence of outliers in the dataset. We also highlight the connections that may exist with minimum distance estimators using L2-distance. Finally, we provide a theoretical study of the stochastic gradient descent algorithm used to compute the estimator, and we support our findings with numerical simulations. We also propose a Bayesian version of our estimator, that we study from both a theoretical and a computational points of view
Wolley, Chirine. "Apprentissage supervisé à partir des multiples annotateurs incertains." Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM4070/document.
Повний текст джерелаIn supervised learning tasks, obtaining the ground truth label for each instance of the training dataset can be difficult, time-consuming and/or expensive. With the advent of infrastructures such as the Internet, an increasing number of web services propose crowdsourcing as a way to collect a large enough set of labels from internet users. The use of these services provides an exceptional facility to collect labels from anonymous annotators, and thus, it considerably simplifies the process of building labels datasets. Nonetheless, the main drawback of crowdsourcing services is their lack of control over the annotators and their inability to verify and control the accuracy of the labels and the level of expertise for each labeler. Hence, managing the annotators' uncertainty is a clue for learning from imperfect annotations. This thesis provides three algorithms when learning from multiple uncertain annotators. IGNORE generates a classifier that predict the label of a new instance and evaluate the performance of each annotator according to their level of uncertainty. X-Ignore, considers that the performance of the annotators both depends on their uncertainty and on the quality of the initial dataset to be annotated. Finally, ExpertS deals with the problem of annotators' selection when generating the classifier. It identifies experts annotators, and learn the classifier based only on their labels. We conducted in this thesis a large set of experiments in order to evaluate our models, both using experimental and real world medical data. The results prove the performance and accuracy of our models compared to previous state of the art solutions in this context
Behúň, Kamil. "Příznaky z videa pro klasifikaci." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236367.
Повний текст джерелаLe, Folgoc Loïc. "Apprentissage statistique pour la personnalisation de modèles cardiaques à partir de données d’imagerie." Thesis, Nice, 2015. http://www.theses.fr/2015NICE4098/document.
Повний текст джерелаThis thesis focuses on the calibration of an electromechanical model of the heart from patient-specific, image-based data; and on the related task of extracting the cardiac motion from 4D images. Long-term perspectives for personalized computer simulation of the cardiac function include aid to the diagnosis, aid to the planning of therapy and prevention of risks. To this end, we explore tools and possibilities offered by statistical learning. To personalize cardiac mechanics, we introduce an efficient framework coupling machine learning and an original statistical representation of shape & motion based on 3D+t currents. The method relies on a reduced mapping between the space of mechanical parameters and the space of cardiac motion. The second focus of the thesis is on cardiac motion tracking, a key processing step in the calibration pipeline, with an emphasis on quantification of uncertainty. We develop a generic sparse Bayesian model of image registration with three main contributions: an extended image similarity term, the automated tuning of registration parameters and uncertainty quantification. We propose an approximate inference scheme that is tractable on 4D clinical data. Finally, we wish to evaluate the quality of uncertainty estimates returned by the approximate inference scheme. We compare the predictions of the approximate scheme with those of an inference scheme developed on the grounds of reversible jump MCMC. We provide more insight into the theoretical properties of the sparse structured Bayesian model and into the empirical behaviour of both inference schemes
Dang, Hong-Phuong. "Approches bayésiennes non paramétriques et apprentissage de dictionnaire pour les problèmes inverses en traitement d'image." Thesis, Ecole centrale de Lille, 2016. http://www.theses.fr/2016ECLI0019/document.
Повний текст джерелаDictionary learning for sparse representation has been widely advocated for solving inverse problems. Optimization methods and parametric approaches towards dictionary learning have been particularly explored. These methods meet some limitations, particularly related to the choice of parameters. In general, the dictionary size is fixed in advance, and sparsity or noise level may also be needed. In this thesis, we show how to perform jointly dictionary and parameter learning, with an emphasis on image processing. We propose and study the Indian Buffet Process for Dictionary Learning (IBP-DL) method, using a bayesian nonparametric approach.A primer on bayesian nonparametrics is first presented. Dirichlet and Beta processes and their respective derivatives, the Chinese restaurant and Indian Buffet processes are described. The proposed model for dictionary learning relies on an Indian Buffet prior, which permits to learn an adaptive size dictionary. The Monte-Carlo method for inference is detailed. Noise and sparsity levels are also inferred, so that in practice no parameter tuning is required. Numerical experiments illustrate the performances of the approach in different settings: image denoising, inpainting and compressed sensing. Results are compared with state-of-the art methods is made. Matlab and C sources are available for sake of reproducibility
Gerchinovitz, Sébastien. "Prédiction de suites individuelles et cadre statistique classique : étude de quelques liens autour de la régression parcimonieuse et des techniques d'agrégation." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00653550.
Повний текст джерелаPrasad, Ranjitha. "Sparse Bayesian Learning For Joint Channel Estimation Data Detection In OFDM Systems." Thesis, 2015. http://etd.iisc.ernet.in/2005/3997.
Повний текст джерелаShi, Minghui. "Bayesian Sparse Learning for High Dimensional Data." Diss., 2011. http://hdl.handle.net/10161/3869.
Повний текст джерелаIn this thesis, we develop some Bayesian sparse learning methods for high dimensional data analysis. There are two important topics that are related to the idea of sparse learning -- variable selection and factor analysis. We start with Bayesian variable selection problem in regression models. One challenge in Bayesian variable selection is to search the huge model space adequately, while identifying high posterior probability regions. In the past decades, the main focus has been on the use of Markov chain Monte Carlo (MCMC) algorithms for these purposes. In the first part of this thesis, instead of using MCMC, we propose a new computational approach based on sequential Monte Carlo (SMC), which we refer to as particle stochastic search (PSS). We illustrate PSS through applications to linear regression and probit models.
Besides the Bayesian stochastic search algorithms, there is a rich literature on shrinkage and variable selection methods for high dimensional regression and classification with vector-valued parameters, such as lasso (Tibshirani, 1996) and the relevance vector machine (Tipping, 2001). Comparing with the Bayesian stochastic search algorithms, these methods does not account for model uncertainty but are more computationally efficient. In the second part of this thesis, we generalize this type of ideas to matrix valued parameters and focus on developing efficient variable selection method for multivariate regression. We propose a Bayesian shrinkage model (BSM) and an efficient algorithm for learning the associated parameters .
In the third part of this thesis, we focus on the topic of factor analysis which has been widely used in unsupervised learnings. One central problem in factor analysis is related to the determination of the number of latent factors. We propose some Bayesian model selection criteria for selecting the number of latent factors based on a graphical factor model. As it is illustrated in Chapter 4, our proposed method achieves good performance in correctly selecting the number of factors in several different settings. As for application, we implement the graphical factor model for several different purposes, such as covariance matrix estimation, latent factor regression and classification.
Dissertation
Huang, Din-Hwa, and 黃汀華. "Basis Adaptive Sparse Bayesian Learning : Algorithms and Applications." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/6n47p5.
Повний текст джерела國立交通大學
電信工程研究所
103
Sparse Bayesian learning (SBL) is a widely used compressive sensing (CS) method that finds the solution by Bayesian inference. In this approach, a basis function is specified to form the transform matrix. For a particular application, it may exist a proper basis, with known model function and unknown parameters, which can convert the signal to a sparse domain. In conventional SBL, the parameters of the basis are assumed to be known as priori. This assumption may not be valid in real-world applications, and the efficacy of conventional SBL approaches can be greatly affected. In this dissertation, we propose a basis-adaptive-sparse-Bayesian-learning (BA-SBL) framework, which can estimate the basis and system parameters, alternatively and iteratively, to solve the problem. Possible applications are also explored. We start the work with the cooperative spectrum sensing problem in cognitive radio (CR) systems. It is known that in addition to spectrum sparsity, spatial sparsity can also be used to further enhance spectral utilization. To achieve that, secondary users (SUs) must know the locations and signal-strength distributions of primary-users’ base-stations (PUBSs), which is referred to as radio source positioning and power-propagation-map (PPM) reconstruction. Conventional approaches approximate PUBSs’ power decay with a path-loss model (PLM) and assume PUBSs’ locations on some grid points. However, the parameters of the PLM have to be known in advance and the estimation accuracy is bounded by the resolution of the grid points. We first employ a Laplacian function to model the PUBS power decay profile and propose a BA-SBL scheme to estimate corresponding parameters. With the proposed method, little priori information is required. To further enhance the performance, we incorporate source number detection methods such that the number of the PUBSs can be precisely detected. Simulations show that the proposed algorithm has satisfactory performance even when the spatial measurement rate is low. While the proposed BA-SBL scheme can effectively reconstruct the PPM in CR systems, it can only be applied in one frequency band at a time, and the frequency-band dependence is not considered. To fill the gap, we then extend the Laplacian function to the multiple-band scenario. For a multi-band Laplacian function, its correlation between different bands is taken into consideration by a block SBL (BSBL) method. The BA-SBL is then modified and extended to a basis-adaptive BSBL (BA-BSBL) scheme, simultaneously reconstructing the PPMs of multiple frequency bands. Simulations show that BA-BSBL outperforms BA-SBL applied to each band, independently. Finally, we apply the proposed BA-BSBL procedure to the positioning problem in the 3rdgeneration-partnership-project (3GPP) long-term-evolution (LTE) systems. The observed-timedifference-of-arrival (OTDOA) method is used to estimate the location of user-element (UE). It uses the estimated time-of-arrivals (TOAs) from three different base stations (BSs) as the observations. The TOA corresponding to a BS can be obtained by the first-tap delay of the time-domain channel response. The main problem of conventional OTDOA methods is that the precision of TOA estimation, obtained by a channel estimation method, is limited by the quantization effect of the receiver’s sampler. Since wireless channels are generally spare, we can then formulate the time-domain channel estimation as a CS problem. Using the pulseshaping-filter response as the basis, we apply the proposed BA-BSBL procedure to conduct the channel estimation, and the TOA can be estimated without quantization. Simulations show that the proposed BA-BSBL algorithm can significantly enhance the precision of TOA estimation and then improve the positioning performance.
Huang, Wen-Han, and 黃玟翰. "Three-dimensional probabilistic site characterization by sparse Bayesian learning." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/6u62y3.
Повний текст джерела國立臺灣大學
土木工程學研究所
107
This study investigated the modified cone tip resistance (qt) data from cone penetration tests (CPT). The feasibility and method of identifying the trend function were also discussed. The vertical spatial distribution is expressed as a depth-dependent trend function and a zero-mean spatial variation. Trend function can help us catch soil properties in space. Spatial variation can be estimated by standard deviation (σ) and scale of fluctuation (δ). In addition to the vertical scale of fluctuation, in 3D case, horizontal scale of fluctuation is also important. However, the number of horizontal data is much less than that of the vertical data. Horizontal scale of fluctuation is hard to be estimated. The estimation of the horizontal parameter is difficult. Another problem is that when analyzing multiple data at a time, the matrix becomes very huge, increasing the computation and even exceeding the load of the memory. We use Cholesky decomposition and Kronecker product to simplify the matrix. In this way, we can greatly reduce the computation. This study uses a two-step Bayesian analysis to identify trend functions. The first step is to select the basis functions we need by sparse Bayesian learning. In this study, we also consider the effects of different kinds of basis functions. The second step is to use transitional Markov chain Monte Carlo (TMCMC; Ching and Chen, 2007) as a method for estimating the parameters of the random field. Through the above two steps, we can fit the trend function and model the random field.
Parisi, Simone. "Reinforcement Learning with Sparse and Multiple Rewards." Phd thesis, 2020. https://tuprints.ulb.tu-darmstadt.de/11372/1/THESIS.PDF.
Повний текст джерелаHuang, Han-Shen, and 黃漢申. "Learning from Sparse Data: An Approach to Parameter Learning in Bayesian Networks." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/18831073237145141413.
Повний текст джерела國立臺灣大學
資訊工程學研究所
91
Many newly-emerging applications with small and incomplete (sparse for abbreviation) data sets present new challenges to machine learning. For example, we would like to have a model that can accurately predict the possibility of domestic terrorist incidents and attack terrorism in advance. Such incidents are rare, but always bring severe impact once they really happen. In addition, the relevant symptoms may be unknown, unobserved, and different case by case. Therefore, learning accurate models from this kind of sparse data is difficult, but very meaningful and important. One way to deal with such situations is to learn probabilistic models from sparse data sets. Probability theory is well-founded for domains with uncertainty and for data sets with missing values. We use the Bayesian network as the modeling tool because of its clear semantics for human experts. The network structure can be determined by the domain experts, showing the causal relations between features. Then, the parameters can be learned from data sets, which is more tedious for human experts. This thesis proposes a search-based approach to the parameter learning problem in Bayesian networks from sparse training sets. A search-based solution consists of the metric and the search algorithm. The most frequently used solution is to search on the data likelihood metric based on Maximum-Likelihood estimation (ML) with the Expectation-Maximization (EM) algorithm or the gradient ascent algorithm. However, our analysis shows that the ML learning for sparse data tends to over/underestimate the probabilities for low/high-frequency states of multinomial random variables. Therefore, we propose Entropic Rectification Function (ERF) to rectify the deviation without prior information about the application domain. The general EM-based framework for penalized data likelihood function, Penalized EM (PEM) algorithm, can search on ERF, but time-consuming numerical methods are required in the M-step. To accelerate the computation, we propose Fixed-Point PEM (FPEM) algorithm, in which there is a closed-form solution for the M-step based on the framework of the fixed-point iteration method. We show that ERF outperforms the data likelihood metric by leading the search algorithms to stop at the estimates with smaller KL divergences to the true distribution, and FPEM outperforms PEM by searching out local maxima faster. In addition, ERF can also be used to learn other probabilistic models with multinomial distributions, like Hidden Markov model. FPEM can search on other penalized data likelihood metrics as well.
Kuen-FengLee and 李昆峯. "Construction of Document Model and Language Model Using Bayesian Sparse Learning." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/57056195766494950616.
Повний текст джерелаTien-Yu, Hsieh, and 謝典佑. "Modeling Students' Learning Bugs and Skills Using Combining Multiple Bayesian Networks." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/06642102650546308286.
Повний текст джерела國立臺中教育大學
數學教育學系
94
The goal of this paper is trying to develop fusion methods for combining multiple Bayesian networks and to obtain better classification results than single Bayesian networks. Six fusion methods, Maximum, Minimum, Average, Product, Majority Vote and Fusion Structure were proposed and evaluated based on educational test data. The results show that the proposed fusion methods, Structure Fusion, with dynamic cut-point selection can improve the classification accuracy.
Kück, Hendrik. "Bayesian formulations of multiple instance learning with applications to general object recognition." Thesis, 2004. http://hdl.handle.net/2429/15680.
Повний текст джерелаManandhar, Achut. "Hierarchical Bayesian Learning Approaches for Different Labeling Cases." Diss., 2015. http://hdl.handle.net/10161/11321.
Повний текст джерелаThe goal of a machine learning problem is to learn useful patterns from observations so that appropriate inference can be made from new observations as they become available. Based on whether labels are available for training data, a vast majority of the machine learning approaches can be broadly categorized into supervised or unsupervised learning approaches. In the context of supervised learning, when observations are available as labeled feature vectors, the learning process is a well-understood problem. However, for many applications, the standard supervised learning becomes complicated because the labels for observations are unavailable as labeled feature vectors. For example, in a ground penetrating radar (GPR) based landmine detection problem, the alarm locations are only known in 2D coordinates on the earth's surface but unknown for individual target depths. Typically, in order to apply computer vision techniques to the GPR data, it is convenient to represent the GPR data as a 2D image. Since a large portion of the image does not contain useful information pertaining to the target, the image is typically further subdivided into subimages along depth. These subimages at a particular alarm location can be considered as a set of observations, where the label is only available for the entire set but unavailable for individual observations along depth. In the absence of individual observation labels, for the purposes of training standard supervised learning approaches, observations both above and below the target are labeled as targets despite substantial differences in their characteristics. As a result, the label uncertainty with depth would complicate the parameter inference in the standard supervised learning approaches, potentially degrading their performance. In this work, we develop learning algorithms for three such specific scenarios where: (1) labels are only available for sets of independent and identically distributed (i.i.d.) observations, (2) labels are only available for sets of sequential observations, and (3) continuous correlated multiple labels are available for spatio-temporal observations. For each of these scenarios, we propose a modification in a traditional learning approach to improve its predictive accuracy. The first two algorithms are based on a set-based framework called as multiple instance learning (MIL) whereas the third algorithm is based on a structured output-associative regression (SOAR) framework. The MIL approaches are motivated by the landmine detection problem using GPR data, where the training data is typically available as labeled sets of observations or sets of sequences. The SOAR learning approach is instead motivated by the multi-dimensional human emotion label prediction problem using audio-visual data, where the training data is available in the form of multiple continuous correlated labels representing complex human emotions. In both of these applications, the unavailability of the training data as labeled featured vectors motivate developing new learning approaches that are more appropriate to model the data.
A large majority of the existing MIL approaches require computationally expensive parameter optimization, do not generalize well with time-series data, and are incapable of online learning. To overcome these limitations, for sets of observations, this work develops a nonparametric Bayesian approach to learning in MIL scenarios based on Dirichlet process mixture models. The nonparametric nature of the model and the use of non-informative priors remove the need to perform cross-validation based optimization while variational Bayesian inference allows for rapid parameter learning. The resulting approach is highly generalizable and also capable of online learning. For sets of sequences, this work integrates Hidden Markov models (HMMs) into an MIL framework and develops a new approach called the multiple instance hidden Markov model. The model parameters are inferred using variational Bayes, making the model tractable and computationally efficient. The resulting approach is highly generalizable and also capable of online learning. Similarly, most of the existing approaches developed for modeling multiple continuous correlated emotion labels do not model the spatio-temporal correlation among the emotion labels. Few approaches that do model the correlation fail to predict the multiple emotion labels simultaneously, resulting in latency during testing, and potentially compromising the effectiveness of implementing the approach in real-time scenario. This work integrates the output-associative relevance vector machine (OARVM) approach with the multivariate relevance vector machine (MVRVM) approach to simultaneously predict multiple emotion labels. The resulting approach performs competitively with the existing approaches while reducing the prediction time during testing, and the sparse Bayesian inference allows for rapid parameter learning. Experimental results on several synthetic datasets, benchmark datasets, GPR-based landmine detection datasets, and human emotion recognition datasets show that our proposed approaches perform comparably or better than the existing approaches.
Dissertation
"Bayesian Framework for Sparse Vector Recovery and Parameter Bounds with Application to Compressive Sensing." Master's thesis, 2019. http://hdl.handle.net/2286/R.I.55639.
Повний текст джерелаDissertation/Thesis
Masters Thesis Computer Engineering 2019
Yang, Chih-Wei, and 楊智為. "Modeling Student’s Learning Bugs and Skills Using Combining Multiple Modeling Student’s Learning Bugs and Skills Using Combining Bayesian Networks based on SVM." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/14681956954826606265.
Повний текст джерела國立臺中教育大學
教育測驗統計研究所
95
The goal of this study was trying to combine the multiple Bayesian networks with classifier and to obtain better accuracy than single Bayesian network. The two classifiers, k-nearest-neighborhood, and support vector machine were used in this paper with two kinds of input, binary and posterior probability. The results showed that basing on the support vector machine to combine the multiple Bayesian networks with posterior probability can improve the classification accuracy.
Rodrigues, Filipe Manuel Pereira Duarte. "Probabilistic models for learning from crowdsourced data." Doctoral thesis, 2016. http://hdl.handle.net/10316/29454.
Повний текст джерелаA presente tese propõe um conjunto de modelos probabilísticos para aprendizagem a partir de dados gerados pela multidão (crowd). Este tipo de dados tem vindo rapidamente a alterar a forma como muitos problemas de aprendizagem máquina são abordados em diferentes áreas do domínio científico, tais como o processamento de linguagem natural, a visão computacional e a música. Através da sabedoria e conhecimento da crowd, foi possível na área de aprendizagem máquina o desenvolvimento de abordagens para realizar tarefas complexas de uma forma muito mais escalável. Por exemplo, as plataformas de crowdsourcing como o Amazon mechanical turk (AMT) colocam ao dispor dos seus utilizadores um recurso acessível e económico para etiquetar largos conjuntos de dados de forma eficiente. Contudo, os diferentes vieses e níveis de perícia individidual dos diversos anotadores que contribuem nestas plataformas tornam necessário o desenvolvimento de abordagens específicas e direcionadas para este tipo de dados multi-anotador. Tendo em mente o problema da heterogeneidade dos anotadores, começamos por introduzir uma classe de modelos de conhecimento latente. Estes modelos são capazes de diferenciar anotadores confiáveis de anotadores cujas respostas são dadas de forma aleatória ou pouco premeditada, sem que para isso seja necessário ter acesso às respostas verdadeiras, ao mesmo tempo que é treinado um classificador de regressão logística ou um conditional random field. De seguida, são considerados modelos de crescente complexidade, desenvolvendo-se uma generalização dos classificadores baseados em processos Gaussianos para configurações multi-anotador. Estes modelos permitem aprender fronteiras de decisão não lineares entre classes, bem como o desenvolvimento de metodologias de aprendizagem activa, que são capazes de aumentar a eficiência do crowdsourcing e reduzir os custos associados. Por último, tendo em conta que a grande maioria das tarefas para as quais o crowdsourcing é usado envolvem dados complexos e de elevada dimensionalidade tais como texto ou imagens, são propostos dois modelos de tópicos supervisionados: um, para problemas de classificação e, outro, para regressão. A superioridade das modelos acima mencionados sobre as abordagens do estado da arte é empiricamente demonstrada usando dados reais recolhidos do AMT para diferentes tarefas como a classificação de posts, notícias, imagens e música, ou até mesmo na previsão do sentimento latente num texto e da atribuição do número de estrelas a um restaurante ou a um filme. Contudo, o conceito de crowdsourcing não se limita a plataformas dedicadas como o AMT. Basta considerarmos os aspectos sociais da Web moderna, que rapidamente começamos a compreender a verdadeira natureza ubíqua do crowdsourcing. Essa componente social da Web deu origem a um mundo de possibilidades estimulantes na área de inteligência artificial em geral. Por exemplo, da perspectiva dos sistemas inteligentes de transportes, a informação partilhada online por multidões fornece o contexto que nos dá a possibilidade de perceber melhor como as pessoas se movem em espaços urbanos. Na segunda parte desta tese, são usados dados gerados pela crowd como entradas adicionais de forma a melhorar modelos de aprendizagem máquina. Nomeadamente, é considerado o problema de compreender a procura em sistemas de transportes na presença de eventos, tais como concertos, eventos desportivos ou festivais. Inicialmente, é desenvolvido um modelo probabilístico para explicar sobrelotações anormais usando informação recolhida da Web. De seguida, é proposto um modelo Bayesiano aditivo cujas componentes são processos Gaussianos. Utilizando dados reais do sistema de transportes públicos de Singapura e dados gerados na Web sobre eventos, verificamos empiricamente a qualidade superior das previsões do modelo proposto em relação a abordagens do estado da arte. Além disso, devido à formulação aditiva do modelo proposto, verificamos que este é capaz de desagregar uma série temporal de procura de transportes numa componente de rotina (e.g. devido à mobilidade pendular) e nas componentes que correspondem às contribuições dos vários eventos individuais identificados. No geral, os modelos propostos nesta tese para aprender com base em dados gerados pela crowd são de vasta aplicabilidade e de grande valor para um amplo espectro de comunidades científicas.
This thesis leverages the general framework of probabilistic graphical models to develop probabilistic approaches for learning from crowdsourced data. This type of data is rapidly changing the way we approach many machine learning problems in different areas such as natural language processing, computer vision and music. By exploiting the wisdom of crowds, machine learning researchers and practitioners are able to develop approaches to perform complex tasks in a much more scalable manner. For instance, crowdsourcing platforms like Amazon mechanical turk provide users with an inexpensive and accessible resource for labeling large datasets efficiently. However, the different biases and levels of expertise that are commonly found among different annotators in these platforms deem the development of targeted approaches necessary. With the issue of annotator heterogeneity in mind, we start by introducing a class of latent expertise models which are able to discern reliable annotators from random ones without access to the ground truth, while jointly learning a logistic regression classifier or a conditional random field. Then, a generalization of Gaussian process classifiers to multiple-annotator settings is developed, which makes it possible to learn non-linear decision boundaries between classes and to develop an active learning methodology that is able to increase the efficiency of crowdsourcing while reducing its cost. Lastly, since the majority of the tasks for which crowdsourced data is commonly used involves complex high-dimensional data such as images or text, two supervised topic models are also proposed, one for classification and another for regression problems. Using real crowdsourced data from Mechanical Turk, we empirically demonstrate the superiority of the aforementioned models over state-of-the-art approaches in many different tasks such as classifying posts, news stories, images and music, or even predicting the sentiment of a text, the number of stars of a review or the rating of movie. But the concept of crowdsourcing is not limited to dedicated platforms such as Mechanical Turk. For example, if we consider the social aspects of the modern Web, we begin to perceive the true ubiquitous nature of crowdsourcing. This opened up an exciting new world of possibilities in artificial intelligence. For instance, from the perspective of intelligent transportation systems, the information shared online by crowds provides the context that allows us to better understand how people move in urban environments. In the second part of this thesis, we explore the use of data generated by crowds as additional inputs in order to improve machine learning models. Namely, the problem of understanding public transport demand in the presence of special events such as concerts, sports games or festivals, is considered. First, a probabilistic model is developed for explaining non-habitual overcrowding using crowd-generated information mined from the Web. Then, a Bayesian additive model with Gaussian process components is proposed. Using real data from Singapore's transport system and crowd-generated data regarding special events, this model is empirically shown to be able to outperform state-of-the-art approaches for predicting public transport demand. Furthermore, due to its additive formulation, the proposed model is able to breakdown an observed time-series of transport demand into a routine component corresponding to commuting and the contributions of individual special events. Overall, the models proposed in this thesis for learning from crowdsourced data are of wide applicability and can be of great value to a broad range of research communities.
FCT - SFRH/BD/78396/2011
chang, chen jung, and 陳榮昌. "Adaptive learning system based on Bayesian network using fusion strategy for combining multiple student models -using compound shape for an example." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/50476915610782026926.
Повний текст джерела亞洲大學
資訊工程學系碩士班
95
The main purpose of the research is to explore the educational assessment on the basis of Evidence-Centered Design(ECD) to build a convenient and effective diagnosis system. We use multiple Bayesian networks for modeling assessment data and identifying bugs and sub-skills in The “Compound Shape” of Mathematics in Grade 6. This research integrates the opinion of the experts, scholars and primary school teachers. Also, the multimedia computer is devised for Diagnostic Testing and computerizes adaptive remedial instruction with the system. Students can receive not only individual diagnostic tests. But adequate and in-time computerized adaptive remedial instruction. Evaluation Diagnosis and remedy can be achieved simultaneously. The findings of this research are as follows: 1. Multiple Bayesian networks did enhance the recognition level. 2. The system could diagnose students’ errors, which shows that the adaptive test based on the multiple Bayesian networks was effective. 3. The computerized adaptive remedial instruction was testified to be able to replace written tests in a convenient and time-saving way. 4. After adopting computerized adaptive remedial instruction, the bugs of most students were reduced and their skills were improved. It revealed that the computerized adaptive remedial instruction did help enhance students’ learning effects. 5. The distribution of students’ bugs and skills varied with districts where the schools were located and the teachers’ teaching methods.
Srinivas, Suraj. "Learning Compact Architectures for Deep Neural Networks." Thesis, 2017. http://etd.iisc.ernet.in/2005/3581.
Повний текст джерелаDivya, Padmanabhan. "New Methods for Learning from Heterogeneous and Strategic Agents." Thesis, 2017. http://etd.iisc.ernet.in/2005/3562.
Повний текст джерела(8086652), Guilherme Maia Rodrigues Gomes. "Hypothesis testing and community detection on networks with missingness and block structure." Thesis, 2019.
Знайти повний текст джерелаAshofteh, Afshin. "Data Science for Finance: Targeted Learning from (Big) Data to Economic Stability and Financial Risk Management." Doctoral thesis, 2022. http://hdl.handle.net/10362/135620.
Повний текст джерелаThe modelling, measurement, and management of systemic financial stability remains a critical issue in most countries. Policymakers, regulators, and managers depend on complex models for financial stability and risk management. The models are compelled to be robust, realistic, and consistent with all relevant available data. This requires great data disclosure, which is deemed to have the highest quality standards. However, stressed situations, financial crises, and pandemics are the source of many new risks with new requirements such as new data sources and different models. This dissertation aims to show the data quality challenges of high-risk situations such as pandemics or economic crisis and it try to theorize the new machine learning models for predictive and longitudes time series models. In the first study (Chapter Two) we analyzed and compared the quality of official datasets available for COVID-19 as a best practice for a recent high-risk situation with dramatic effects on financial stability. We used comparative statistical analysis to evaluate the accuracy of data collection by a national (Chinese Center for Disease Control and Prevention) and two international (World Health Organization; European Centre for Disease Prevention and Control) organizations based on the value of systematic measurement errors. We combined excel files, text mining techniques, and manual data entries to extract the COVID-19 data from official reports and to generate an accurate profile for comparisons. The findings show noticeable and increasing measurement errors in the three datasets as the pandemic outbreak expanded and more countries contributed data for the official repositories, raising data comparability concerns and pointing to the need for better coordination and harmonized statistical methods. The study offers a COVID-19 combined dataset and dashboard with minimum systematic measurement errors and valuable insights into the potential problems in using databanks without carefully examining the metadata and additional documentation that describe the overall context of data. In the second study (Chapter Three) we discussed credit risk as the most significant source of risk in banking as one of the most important sectors of financial institutions. We proposed a new machine learning approach for online credit scoring which is enough conservative and robust for unstable and high-risk situations. This Chapter is aimed at the case of credit scoring in risk management and presents a novel method to be used for the default prediction of high-risk branches or customers. This study uses the Kruskal-Wallis non-parametric statistic to form a conservative credit-scoring model and to study its impact on modeling performance on the benefit of the credit provider. The findings show that the new credit scoring methodology represents a reasonable coefficient of determination and a very low false-negative rate. It is computationally less expensive with high accuracy with around 18% improvement in Recall/Sensitivity. Because of the recent perspective of continued credit/behavior scoring, our study suggests using this credit score for non-traditional data sources for online loan providers to allow them to study and reveal changes in client behavior over time and choose the reliable unbanked customers, based on their application data. This is the first study that develops an online non-parametric credit scoring system, which can reselect effective features automatically for continued credit evaluation and weigh them out by their level of contribution with a good diagnostic ability. In the third study (Chapter Four) we focus on the financial stability challenges faced by insurance companies and pension schemes when managing systematic (undiversifiable) mortality and longevity risk. For this purpose, we first developed a new ensemble learning strategy for panel time-series forecasting and studied its applications to tracking respiratory disease excess mortality during the COVID-19 pandemic. The layered learning approach is a solution related to ensemble learning to address a given predictive task by different predictive models when direct mapping from inputs to outputs is not accurate. We adopt a layered learning approach to an ensemble learning strategy to solve the predictive tasks with improved predictive performance and take advantage of multiple learning processes into an ensemble model. In this proposed strategy, the appropriate holdout for each model is specified individually. Additionally, the models in the ensemble are selected by a proposed selection approach to be combined dynamically based on their predictive performance. It provides a high-performance ensemble model to automatically cope with the different kinds of time series for each panel member. For the experimental section, we studied more than twelve thousand observations in a portfolio of 61-time series (countries) of reported respiratory disease deaths with monthly sampling frequency to show the amount of improvement in predictive performance. We then compare each country’s forecasts of respiratory disease deaths generated by our model with the corresponding COVID-19 deaths in 2020. The results of this large set of experiments show that the accuracy of the ensemble model is improved noticeably by using different holdouts for different contributed time series methods based on the proposed model selection method. These improved time series models provide us proper forecasting of respiratory disease deaths for each country, exhibiting high correlation (0.94) with Covid-19 deaths in 2020. In the fourth study (Chapter Five) we used the new ensemble learning approach for time series modeling, discussed in the previous Chapter, accompany by K-means clustering for forecasting life tables in COVID-19 times. Stochastic mortality modeling plays a critical role in public pension design, population and public health projections, and in the design, pricing, and risk management of life insurance contracts and longevity-linked securities. There is no general method to forecast the mortality rate applicable to all situations especially for unusual years such as the COVID-19 pandemic. In this Chapter, we investigate the feasibility of using an ensemble of traditional and machine learning time series methods to empower forecasts of age-specific mortality rates for groups of countries that share common longevity trends. We use Generalized Age-Period-Cohort stochastic mortality models to capture age and period effects, apply K-means clustering to time series to group countries following common longevity trends, and use ensemble learning to forecast life expectancy and annuity prices by age and sex. To calibrate models, we use data for 14 European countries from 1960 to 2018. The results show that the ensemble method presents the best robust results overall with minimum RMSE in the presence of structural changes in the shape of time series at the time of COVID-19. In this dissertation’s conclusions (Chapter Six), we provide more detailed insights about the overall contributions of this dissertation on the financial stability and risk management by data science, opportunities, limitations, and avenues for future research about the application of data science in finance and economy.