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Relevant bibliographies by topics / Bayesian recovery / Dissertations / Theses

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Dissertations / Theses on the topic 'Bayesian recovery'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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1

Tan, Xing. "Bayesian sparse signal recovery." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0041176.

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2

Karseras, Evripidis. "Hierarchical Bayesian models for sparse signal recovery and sampling." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/32102.

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This thesis builds upon the problem of sparse signal recovery from the Bayesian standpoint. The advantages of employing Bayesian models are underscored, with the most important being the ease at which a model can be expanded or altered; leading to a fresh class of algorithms. The thesis fills out several gaps between sparse recovery algorithms and sparse Bayesian models; firstly the lack of global performance guarantees for the latter and secondly what the signifying differences are between the two. These questions are answered by providing; a refined theoretical analysis and a new class of al

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3

Echavarria, Gregory Maria Angelica. "Predictive Data-Derived Bayesian Statistic-Transport Model and Simulator of Sunken Oil Mass." Scholarly Repository, 2010. http://scholarlyrepository.miami.edu/oa_dissertations/471.

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Sunken oil is difficult to locate because remote sensing techniques cannot as yet provide views of sunken oil over large areas. Moreover, the oil may re-suspend and sink with changes in salinity, sediment load, and temperature, making deterministic fate models difficult to deploy and calibrate when even the presence of sunken oil is difficult to assess. For these reasons, together with the expense of field data collection, there is a need for a statistical technique integrating limited data collection with stochastic transport modeling. Predictive Bayesian modeling techniques have been deve

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4

Tang, Man. "Bayesian population dynamics modeling to guide population restoration and recovery of endangered mussels in the Clinch River, Tennessee and Virginia." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/49598.

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Freshwater mussels have played an important role in the history of human culture and also in ecosystem functioning. But during the past several decades, the abundance and diversity of mussel species has declined all over the world. To address the urgent need to maintain and restore populations of endangered freshwater mussels, quantitative population dynamics modeling is needed to evaluate population status and guide the management of endangered freshwater mussels. One endangered mussel species, the oyster mussel (Epioblasma capsaeformis), was selected to study its population dynamics for my r

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5

Cave, Vanessa M. "Statistical models for the long-term monitoring of songbird populations : a Bayesian analysis of constant effort sites and ring-recovery data." Thesis, St Andrews, 2010. http://hdl.handle.net/10023/885.

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Dine, James. "A habitat suitability model for Ricord's iguana in the Dominican Republic." Connect to resource online, 2009. http://hdl.handle.net/1805/1889.

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Thesis (M.S.)--Indiana University, 2009.<br>Title from screen (viewed on August 27, 2009). Department of Geography, Indiana University-Purdue University Indianapolis (IUPUI). Advisor(s): Jan Ramer, Aniruddha Banergee, Jeffery Wilson. Includes vita. Includes bibliographical references (leaves 47-52).

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7

Sugimoto, Tatsuhiro. "Anelastic Strain Recovery Method for In-situ Stress Measurements： A novel analysis procedure based on Bayesian statistical modeling and application to active fault drilling." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263637.

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8

Chen, Cong. "High-Dimensional Generative Models for 3D Perception." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103948.

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Modern robotics and automation systems require high-level reasoning capability in representing, identifying, and interpreting the three-dimensional data of the real world. Understanding the world's geometric structure by visual data is known as 3D perception. The necessity of analyzing irregular and complex 3D data has led to the development of high-dimensional frameworks for data learning. Here, we design several sparse learning-based approaches for high-dimensional data that effectively tackle multiple perception problems, including data filtering, data recovery, and data retrieval. The fram

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SEDDA, GIULIA. "The interplay between movement and perception: how interaction can influence sensorimotor performance and neuromotor recovery." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1011732.

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Movement and perception interact continuously in daily activities. Motor output changes the outside world and affect perceptual representations. Similarly, perception has consequences on movement. Nevertheless, how movement and perception influence each other and share information is still an open question. Mappings from movement to perceptual outcome and vice versa change continuously throughout life. For example, a cerebrovascular accident (stroke) elicits in the nervous system a complex series of reorganization processes at various levels and with different temporal scales. Functiona

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10

Quer, Giorgio. "Optimization of Cognitive Wireless Networks using Compressive Sensing and Probabilistic Graphical Models." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3421992.

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In-network data aggregation to increase the efficiency of data gathering solutions for Wireless Sensor Networks (WSNs) is a challenging task. In the first part of this thesis, we address the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a Data Collection Point (DCP). We exploit Principal Component Analysis (PCA) to learn the relevant statistical characteristics of the signals of interest at the DCP. Then, at the DCP we use this knowledge to design a matrix required by the recovery techniques, that exploit convex optimization (Co

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11

Maurent, Eliott. "Des forêts tropicales et des humains dans les Amériques : trajectoires de réponse aux perturbations anthropiques de la diversité et de la composition des arbres. Of tropical forests and humans in the Americas : response trajectories of tree diversity and composition to anthropogenic disturbances." Electronic Thesis or Diss., Paris, AgroParisTech, 2023. http://www.theses.fr/2023AGPT0014.

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Les forêts tropicales sont confrontées à des perturbations anthropiques fréquentes et intenses, telles que l'exploitation sélective - l'abattage de quelques arbres dans des forêts anciennes, tandis que le reste du peuplement se régénère naturellement. De nombreuses études ont été menées sur la reconstitution des stocks de carbone et de bois, en raison d'un intérêt pour l'atténuation du changement climatique et la rentabilité de l'exploitation. Malgré le rôle crucial de la biodiversité pour le maintien et le fonctionnement des écosystèmes - et sa valeur intrinsèque - peu d'études ont été menées

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12

Antelo, Junior Ernesto Willams Molina. "Estimação conjunta de atraso de tempo subamostral e eco de referência para sinais de ultrassom." Universidade Tecnológica Federal do Paraná, 2017. http://repositorio.utfpr.edu.br/jspui/handle/1/2616.

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CAPES<br>Em ensaios não destrutivos por ultrassom, o sinal obtido a partir de um sistema de aquisição de dados real podem estar contaminados por ruído e os ecos podem ter atrasos de tempo subamostrais. Em alguns casos, esses aspectos podem comprometer a informação obtida de um sinal por um sistema de aquisição. Para lidar com essas situações, podem ser utilizadas técnicas de estimativa de atraso temporal (Time Delay Estimation ou TDE) e também técnicas de reconstrução de sinais, para realizar aproximações e obter mais informações sobre o conjunto de dados. As técnicas de TDE podem ser utiliza

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13

Al-Rabah, Abdullatif R. "Bayesian Recovery of Clipped OFDM Signals: A Receiver-based Approach." Thesis, 2013. http://hdl.handle.net/10754/291094.

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Recently, orthogonal frequency-division multiplexing (OFDM) has been adopted for high-speed wireless communications due to its robustness against multipath fading. However, one of the main fundamental drawbacks of OFDM systems is the high peak-to-average-power ratio (PAPR). Several techniques have been proposed for PAPR reduction. Most of these techniques require transmitter-based (pre-compensated) processing. On the other hand, receiver-based alternatives would save the power and reduce the transmitter complexity. By keeping this in mind, a possible approach is to limit the amplitude of the O

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14

Khanna, Saurabh. "Bayesian Techniques for Joint Sparse Signal Recovery: Theory and Algorithms." Thesis, 2018. https://etd.iisc.ac.in/handle/2005/5292.

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This thesis contributes new theoretical results, solution concepts, and algorithms concerning the Bayesian recovery of multiple joint sparse vectors from noisy and underdetermined linear measurements. The thesis is written in two parts. The first part focuses on the recovery of nonzero support of multiple joint sparse vectors from their linear compressive measurements, an important canonical problem in multisensor signal processing. The support recovery performance of a well known Bayesian inference technique called Multiple Sparse Bayesian Learning (MSBL) is analyzed using tools from lar

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15

"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.

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abstract: Signal compressed using classical compression methods can be acquired using brute force (i.e. searching for non-zero entries in component-wise). However, sparse solutions require combinatorial searches of high computations. In this thesis, instead, two Bayesian approaches are considered to recover a sparse vector from underdetermined noisy measurements. The first is constructed using a Bernoulli-Gaussian (BG) prior distribution and is assumed to be the true generative model. The second is constructed using a Gamma-Normal (GN) prior distribution and is, therefore, a different (i.e. mi

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16

Masood, Mudassir. "Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications." Diss., 2015. http://hdl.handle.net/10754/553050.

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Compressed sensing has been a very active area of research and several elegant algorithms have been developed for the recovery of sparse signals in the past few years. However, most of these algorithms are either computationally expensive or make some assumptions that are not suitable for all real world problems. Recently, focus has shifted to Bayesian-based approaches that are able to perform sparse signal recovery at much lower complexity while invoking constraint and/or a priori information about the data. While Bayesian approaches have their advantages, these methods must have access to a

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17

Sana, Furrukh. "Efficient Techniques of Sparse Signal Analysis for Enhanced Recovery of Information in Biomedical Engineering and Geosciences." Diss., 2016. http://hdl.handle.net/10754/621865.

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Sparse signals are abundant among both natural and man-made signals. Sparsity implies that the signal essentially resides in a small dimensional subspace. The sparsity of the signal can be exploited to improve its recovery from limited and noisy observations. Traditional estimation algorithms generally lack the ability to take advantage of signal sparsity. This dissertation considers several problems in the areas of biomedical engineering and geosciences with the aim of enhancing the recovery of information by exploiting the underlying sparsity in the problem. The objective is to overcome the

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18

Prasanna, Dheeraj. "Structured Sparse Signal Recovery for mmWave Channel Estimation: Intra-vector Correlation and Modulo Compressed Sensing." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5215.

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This thesis contributes new theoretical results and recovery algorithms for the area of sparse signal recovery motivated by applications to the problem of channel estimation in mmWave communication systems. The presentation is in two parts. The first part focuses on the recovery of sparse vectors with correlated non-zero entries from their noisy low dimensional projections. Such structured sparse signals can be recovered using the technique of covariance matching. Here, we first estimate the covariance of the signal from the compressed measurements, and then use the obtained covariance matr

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19

Dine, James. "A Habitat Suitability Model for Ricord’s Iguana in the Dominican Republic." Thesis, 2009. http://hdl.handle.net/1805/1889.

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Indiana University-Purdue University Indianapolis (IUPUI)<br>The West Indian iguanas of the genus Cyclura are the most endangered group of lizards in the world (Burton & Bloxam, 2002). The Ricord’s iguana, Cyclura ricordii, is listed as critically endangered by the International Union for Conservation of Nature (IUCN) (Ramer, 2004). This species is endemic to the island of Hispaniola (Figure 1), and can only be found in limited geographic areas (Burton & Bloxam, 2002). The range of this species is estimated to be only 60% of historical levels, with most areas being affected by some level of di

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