Relevant bibliographies by topics / Bayesian recovery

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  2. Dissertations / Theses
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Academic literature on the topic 'Bayesian recovery'

Author: Grafiati
Published: 6 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "Bayesian recovery"

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Zhao, Juan, Xia Bai, Tao Shan, and Ran Tao. "Block Sparse Bayesian Recovery with Correlated LSM Prior." Wireless Communications and Mobile Computing 2021 (October 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/9942694.

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Compressed sensing can recover sparse signals using a much smaller number of samples than the traditional Nyquist sampling theorem. Block sparse signals (BSS) with nonzero coefficients occurring in clusters arise naturally in many practical scenarios. Utilizing the sparse structure can improve the recovery performance. In this paper, we consider recovering arbitrary BSS with a sparse Bayesian learning framework by inducing correlated Laplacian scale mixture (LSM) prior, which can model the dependence of adjacent elements of the block sparse signal, and then a block sparse Bayesian learning alg
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Wang, Haitao, Qunyi He, Shiwei Peng, and Xiangyang Zeng. "Indoor Sound Source Localization via Inverse Element-Free Simulation Based on Joint Sparse Recovery." Electronics 13, no. 1 (2023): 69. http://dx.doi.org/10.3390/electronics13010069.

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Indoor sound source localization is a key technique in many engineering applications, and an inverse element-free method based on joint sparse recovery in a Bayesian framework is proposed for reverberant environments. In this method, a discrete wave model is constructed to represent the relationships between the sampled sound pressure and the source intensity distribution, and localization in the reverberant environment is realized via inversion from the wave model. By constructing a compact supporting domain, the source intensity can be sparsely represented in subdomains, and the sparse Bayes
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Calvetti, D., and E. Somersalo. "Recovery of shapes: hypermodels and Bayesian learning." Journal of Physics: Conference Series 124 (July 1, 2008): 012014. http://dx.doi.org/10.1088/1742-6596/124/1/012014.

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Sun, Shouwang, Sheng Jiao, Qi Hu, et al. "Missing Structural Health Monitoring Data Recovery Based on Bayesian Matrix Factorization." Sustainability 15, no. 4 (2023): 2951. http://dx.doi.org/10.3390/su15042951.

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The exposure of bridge health-monitoring systems to extreme conditions often results in missing data, which constrains the health monitoring system from working. Therefore, there is an urgent need for an efficient data cleaning method. With the development of big data and machine-learning techniques, several methods for missing-data recovery have emerged. However, optimization-based methods may experience overfitting and demand extensive tuning of parameters, and trained models may still have substantial errors when applied to unseen datasets. Furthermore, many methods can only process monitor
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Johnson, Michael-David, Jacques Cuenca, Timo Lähivaara, et al. "Bayesian reconstruction of surface shape from phaseless scattered acoustic data." Journal of the Acoustical Society of America 156, no. 6 (2024): 4024–36. https://doi.org/10.1121/10.0034549.

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The recovery of the properties or geometry of a rough surface from scattered sound is of interest in many applications, including medicine, water engineering, or structural health monitoring. Existing approaches to reconstruct the roughness profile of a scattering surface based on wave scattering have no intrinsic way of predicting the uncertainty of the reconstruction. In an attempt to recover this uncertainty, a Bayesian framework, and more explicitly, an adaptive Metropolis scheme, is used to infer the properties of a rough surface, parameterised as a superposition of sinusoidal components.
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Gan, Wei, Lu-ping Xu, Zhe Su, and Hua Zhang. "Bayesian Hypothesis Testing Based Recovery for Compressed Sensing." Journal of Electronics & Information Technology 33, no. 11 (2011): 2640–46. http://dx.doi.org/10.3724/sp.j.1146.2011.00151.

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Long, Zhen, Ce Zhu, Jiani Liu, and Yipeng Liu. "Bayesian Low Rank Tensor Ring for Image Recovery." IEEE Transactions on Image Processing 30 (2021): 3568–80. http://dx.doi.org/10.1109/tip.2021.3062195.

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Korki, Mehdi, Hadi Zayyani, and Jingxin Zhang. "Bayesian Hypothesis Testing for Block Sparse Signal Recovery." IEEE Communications Letters 20, no. 3 (2016): 494–97. http://dx.doi.org/10.1109/lcomm.2016.2518169.

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Brooks, S. P., E. A. Catchpole, B. J. T. Morgan, and S. C. Barry. "On the Bayesian Analysis of Ring-Recovery Data." Biometrics 56, no. 3 (2000): 951–56. http://dx.doi.org/10.1111/j.0006-341x.2000.00951.x.

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Wang, Lu, Lifan Zhao, Guoan Bi, and Chunru Wan. "Hierarchical Sparse Signal Recovery by Variational Bayesian Inference." IEEE Signal Processing Letters 21, no. 1 (2014): 110–13. http://dx.doi.org/10.1109/lsp.2013.2292589.

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Dissertations / Theses on the topic "Bayesian recovery"

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

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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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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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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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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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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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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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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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Books on the topic "Bayesian recovery"

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Sawada, Tadamasa, Yunfeng Li, and Zygmunt Pizlo. Shape Perception. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.12.

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This chapter provides a review of topics and concepts that are necessary to study and understand 3D shape perception. This includes group theory and their invariants; model-based invariants; Euclidean, affine, and projective geometry; symmetry; inverse problems; simplicity principle; Fechnerian psychophysics; regularization theory; Bayesian inference; shape constancy and shape veridicality; shape recovery; perspective and orthographic projections; camera models; as well as definitions of shape. All concepts are defined and illustrated, and the reader is provided with references providing mathe
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Book chapters on the topic "Bayesian recovery"

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Kosarev, E. L. "Superresolution limit for Signal recovery." In Maximum Entropy and Bayesian Methods. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-7860-8_50.

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Grant, A. I., and K. J. Packer. "Enhanced Information Recovery From Spectroscopic Data Using MaxEnt." In Maximum Entropy and Bayesian Methods. Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-7860-8_24.

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Mongwe, Wilson Tsakane, Rendani Mbuvha, and Tshilidzi Marwala. "Bayesian Detection of Recovery on Charged-Off Loan Accounts." In Bayesian Machine Learning in Quantitative Finance. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88431-3_7.

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Yang, Haiyan, Xiaolin Huang, Cheng Peng, Jie Yang, and Li Li. "A New Bayesian Method for Jointly Sparse Signal Recovery." In Neural Information Processing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70093-9_94.

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Zhou, Xuefeng, Hongmin Wu, Juan Rojas, Zhihao Xu, and Shuai Li. "Learning Policy for Robot Anomaly Recovery Based on Robot Introspection." In Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6263-1_6.

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Molina, Rafael, Aggelos K. Katsaggelos, and Javier Mateos. "Removal of Blocking Artifacts Using a Hierarchical Bayesian Approach." In Signal Recovery Techniques for Image and Video Compression and Transmission. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-6514-4_1.

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Chu, Chu, Guangjun Wen, Zhong Huang, Jian Su, and Yu Han. "Improved Bayesian Method with Collision Recovery for RFID Anti-collision." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24265-7_5.

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Acharya, Prabin, Yue Zhao, and Fangzhou Liu. "A Bayesian-Based Approach for Post-disaster Recovery Estimation Enhancement." In Lecture Notes in Civil Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9223-2_23.

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Huntbatch, Andrew, Su-Lin Lee, David Firmin, and Guang-Zhong Yang. "Bayesian Motion Recovery Framework for Myocardial Phase-Contrast Velocity MRI." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85990-1_10.

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Fu, Shunkai, Sein Minn, and Michel C. Desmarais. "Towards the Efficient Recovery of General Multi-Dimensional Bayesian Network Classifier." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08979-9_2.

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Conference papers on the topic "Bayesian recovery"

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Yi, Ming, Meng Wang, Tianqi Hong, and Dongbo Zhao. "Bayesian High-Rank Hankel Matrix Completion for Nonlinear Synchrophasor Data Recovery." In 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10688876.

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Masood, Mudassir. "Distributed Bayesian Sparse Signal Recovery Algorithm with Minimal Communication Load in Networks." In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-spring62846.2024.10683350.

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Dolat, Meshkat, Andrew D. Wright, Mohammadamin Zarei, Melis S. Duyar, and Michael Short. "Kinetic Modelling and Optimisation of Co2 Capture and Utilisation to Methane on Dual Function Material." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.187825.

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Dual function materials (DFMs) integrate CO2 capture and conversion, offering a streamlined approach to Power-to-Gas (PtG) processes. This study develops a cyclic steady-state model for the DFM-based methanation of CO2 using the finite difference method. The model captures the adsorption, purge, and methanation stages and incorporates a semi-implicit numerical scheme for stability and accuracy. Bayesian optimisation is used to explore operational and design parameters to maximise methane productivity, CO2 conversion, and product purity. Multi-objective optimisation reveals key trade-offs among
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Niinimäki, Kati, Ville Kolehmainen, and Samuli Siltanen. "Bayesian Multiresolution Method for Local Tomography." In Signal Recovery and Synthesis. OSA, 2009. http://dx.doi.org/10.1364/srs.2009.stua2.

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Jalobeanu, André. "Bayesian Vision for Shape Recovery." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2004. http://dx.doi.org/10.1063/1.1835208.

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Pascazio, V., P. Mathieu, and G. Schirinzi. "A bayesian technique for In-SAR phase unwrapping." In Signal Recovery and Synthesis. OSA, 2001. http://dx.doi.org/10.1364/srs.2001.smd3.

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Baskaran, Shyamsunder, and R. P. Millane. "Bayesian Image Reconstruction in X-ray Fiber Diffraction." In Signal Recovery and Synthesis. Optica Publishing Group, 1998. http://dx.doi.org/10.1364/srs.1998.swa.3.

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The structure completion problem in x-ray fiber diffraction analysis, a crystallographic method for studying polymer structures, involves reconstructing an incomplete image from a known part and experimental data in the form of the squared amplitudes of the Fourier coefficients. Formulating this as a Bayesian estimation problem allows explicit expressions for MMSE and MAP estimates to be obtained. Calculations using simulated fiber diffraction data show that the MMSE estimate out- performs current methods that correspond to certain MAP estimates.
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Wu, Chi-hsin, and Peter C. Doerschuk. "Markov random fields as a priori information for image restoration." In Signal Recovery and Synthesis. Optica Publishing Group, 1995. http://dx.doi.org/10.1364/srs.1995.rwc2.

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Markov random fields (MRFs) [1, 2, 3, 4] provide attractive statistical models for multidimensional signals. However, unfortunately, optimal Bayesian estimators tend to require large amounts of computation. We present an approximation to a particular Bayesian estimator which requires much reduced computation and an example illustrating low-light unknown-blur imaging. See [7] for an alternative approximation based on approximating the MRF lattice by a system of trees and for an alternative cost function.
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Doerschuk, Peter C. "X-ray Crystallography as a Bayesian Signal Reconstruction Problem." In Signal Recovery and Synthesis. Optica Publishing Group, 1992. http://dx.doi.org/10.1364/srs.1992.tuc1.

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A new Markov random field based algorithm is proposed for signal reconstruction from Fourier transform magnitude motivated by the data reduction calculations of x-ray crystallography. The purpose of an x-ray crystallography experiment is to determine the position in three dimensional space of each atom in a molecule. The measured data are the magnitudes squared of the Fourier transform of the electron density function of a crystal of the molecule of interest and possibly also of chemical derivatives. The data reduction calculations are a signal reconstruction problem for the three dimensional
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Oh, S., A. B. Milstein, R. P. Millane, C. A. Boiiman, and K. J. Webb. "Three-dimensional Bayesian optical diffusion tomography with source-detector calibration." In Signal Recovery and Synthesis. OSA, 2001. http://dx.doi.org/10.1364/srs.2001.stua2.

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Reports on the topic "Bayesian recovery"

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Granados, Camilo, and Daniel Parra-Amado. Output Gap Measurement after COVID for Colombia: Lessons from a Permanent-Transitory Approach. Banco de la República, 2025. https://doi.org/10.32468/be.1295.

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We estimate the output gap for the Colombian economy explicitly accounting for the COVID-19 period. Our estimates reveal a significant $20$\% decline in the output gap but with a faster recovery compared to previous crises. Our empirical strategy follows a two-stage Bayesian vector autoregressive (BSVAR) model where i) a scaling factor in the reduced form of VAR is used to model extreme data, such as those observed around the COVID-19 period, and ii) permanent and transitory shocks are structurally identified. As a result, we obtain that a single structural shock explains the potential GDP, wh
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Larrahondo, Cristhian, Augusto Chávez, Laura Giles Álvarez, and Leandro Gaston Andrian. The exchange rate passthrough to domestic prices, new evidence from Colombia. Inter-American Development Bank, 2025. https://doi.org/10.18235/0013378.

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This paper calculates the exchange rate pass through (ERPT) with time constant and time varying coefficients for Colombia between 2006 and 2023. It then estimates the ERPT during four specific depreciation events during the period of analysis: the 2008 financial crisis, the 2014-2016 fall in international fuel prices, the COVID-19 pandemic and the post-COVID recovery. A Bayesian Vector Autoregressive model with exogenous variables (BVARX) model with time constant and time varying coefficients is used for the exercise. The results for time constant coefficients show that a 1 percentage point (p
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Kanno, Yoichiro, Dan Preston, Yoichiro Kanno, and Dan Preston. Fisheries inventories at Rocky Mountain National Park to inform cutthroat trout conservation and recreational angling decision post-fire. National Park Service, 2024. http://dx.doi.org/10.36967/2304877.

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The Cameron Peak Fire and East Troublesome Fire of 2020 were the two largest wildfires in Colorado history. They burned approximately 9% of the Rocky Mountain National Park, raising a concern for trout populations that currently support recreational fishing and success of on-going and future efforts to conserve native trout populations. We inventoried habitat characteristics and biological communities at 19 sites in summer of 2021 and a subset of 11 sites in summer of 2022 to characterize wildfire impacts on aquatic resources, with the focus on characterizing trout population responses. There
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