Добірка наукової літератури з теми "Bayesian recovery"
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Статті в журналах з теми "Bayesian recovery"
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.
Повний текст джерелаCalvetti, 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.
Повний текст джерелаGan, 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 (November 14, 2011): 2640–46. http://dx.doi.org/10.3724/sp.j.1146.2011.00151.
Повний текст джерелаLong, 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.
Повний текст джерелаKorki, Mehdi, Hadi Zayyani, and Jingxin Zhang. "Bayesian Hypothesis Testing for Block Sparse Signal Recovery." IEEE Communications Letters 20, no. 3 (March 2016): 494–97. http://dx.doi.org/10.1109/lcomm.2016.2518169.
Повний текст джерелаBrooks, 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 (September 2000): 951–56. http://dx.doi.org/10.1111/j.0006-341x.2000.00951.x.
Повний текст джерелаWang, Lu, Lifan Zhao, Guoan Bi, and Chunru Wan. "Hierarchical Sparse Signal Recovery by Variational Bayesian Inference." IEEE Signal Processing Letters 21, no. 1 (January 2014): 110–13. http://dx.doi.org/10.1109/lsp.2013.2292589.
Повний текст джерелаHuang, Kaide, Yao Guo, Xuemei Guo, and Guoli Wang. "Heterogeneous Bayesian compressive sensing for sparse signal recovery." IET Signal Processing 8, no. 9 (December 2014): 1009–17. http://dx.doi.org/10.1049/iet-spr.2013.0501.
Повний текст джерелаAhmed, Irfan, Aftab Khan, Nasir Ahmad, NasruMinallah, and Hazrat Ali. "Speech Signal Recovery Using Block Sparse Bayesian Learning." Arabian Journal for Science and Engineering 45, no. 3 (August 6, 2019): 1567–79. http://dx.doi.org/10.1007/s13369-019-04080-6.
Повний текст джерелаZhang, Shuanghui, Yongxiang Liu, Xiang Li, and Guoan Bi. "Variational Bayesian Sparse Signal Recovery With LSM Prior." IEEE Access 5 (2017): 26690–702. http://dx.doi.org/10.1109/access.2017.2765831.
Повний текст джерелаДисертації з теми "Bayesian recovery"
Tan, Xing. "Bayesian sparse signal recovery." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0041176.
Повний текст джерелаKarseras, Evripidis. "Hierarchical Bayesian models for sparse signal recovery and sampling." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/32102.
Повний текст джерелаEchavarria, 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.
Повний текст джерелаTang, 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.
Повний текст джерелаPassive integrated transponder (PIT) tags were applied in my fieldwork to monitor the translocation efficiency of E. capsaeformis and Actinonaias pectorosa at Cleveland Islands (CRM 270.8). Hierarchical Bayesian models were developed to address the individual variability and sex-related differences in growth. In model selection, the model considering individual variability and sex-related differences (if a species has sexual dimorphism) yielded the lowest DIC value. The results from the best model showed that the mean asymptotic length and mean growth rate of female E. capsaeformis were 45.34 mm and 0.279, which were higher than values estimated for males (42.09 mm and 0.216). The mean asymptotic length and mean growth rate for A. pectorosa were 104.2 mm and 0.063, respectively.
To test for the existence of individual and sex-related variability in survival and recapture rates, Bayesian models were developed to address the variability in the analysis of the mark-recapture data of E. capsaeformis and A. pectorosa. DIC was used to compare different models. The median survival rates of male E. capsaeformis, female E. capsaeformis and A. pectorosa were high (>87%, >74% and >91%), indicating that the habitat at Cleveland Islands was suitable for these two mussel species within this survey duration. In addition, the median recapture rates for E. capsaeformis and A. pectorosa were >93% and >96%, indicating that the PIT tag technique provided an efficient monitoring approach. According to model comparison results, the non-hierarchical model or the model with sex--related differences (if a species is sexually dimorphic) in survival rate was suggested for analyzing mark-recapture data when sample sizes are small.
Master of Science
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.
Повний текст джерелаDine, James. "A habitat suitability model for Ricord's iguana in the Dominican Republic." Connect to resource online, 2009. http://hdl.handle.net/1805/1889.
Повний текст джерелаTitle 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).
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.
Повний текст джерелаChen, 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.
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.
Повний текст джерелаQuer, 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.
Повний текст джерелаLa combinazione delle informazioni nelle reti di sensori wireless è una soluzione promettente per aumentare l'efficienza delle techiche di raccolta dati. Nella prima parte di questa tesi viene affrontato il problema della ricostruzione di segnali distribuiti tramite la raccolta di un piccolo numero di campioni al punto di raccolta dati (DCP). Viene sfruttato il metodo dell'analisi delle componenti principali (PCA) per ricostruire al DCP le caratteristiche statistiche del segnale di interesse. Questa informazione viene utilizzata al DCP per determinare la matrice richiesta dalle tecniche di recupero che sfruttano algoritmi di ottimizzazione convessa (Compressive Sensing, CS) per ricostruire l'intero segnale da una sua versione campionata. Per integrare questo modello di monitoraggio in un framework di compressione e recupero del segnale, viene applicata la logica del paradigma 'cognitive': prima si osserva la rete; poi dall'osservazione si derivano le statistiche di interesse, che vengono applicate per il recupero del segnale; si sfruttano queste informazioni statistiche per prenderere decisioni e infine si rendono effettive queste decisioni con un controllo in retroazione. Il framework di compressione e recupero con controllo in retroazione è chiamato "Sensing, Compression and Recovery through ONline Estimation" (SCoRe1). L'intero framework è stato implementato in una architettura per WSN detta WSN-control, accessibile da Internet. Le scelte nella progettazione del protocollo sono state giustificate da un'analisi teorica con un approccio di tipo Bayesiano. Nella seconda parte della tesi il paradigma cognitive viene utilizzato per l'ottimizzazione di reti locali wireless (WLAN). L'architetture della rete cognitive viene integrata nello stack protocollare della rete wireless. Nello specifico, vengono utilizzati dei modelli grafici probabilistici per modellare lo stack protocollare: le relazioni probabilistiche tra alcuni parametri di diversi livelli vengono studiate con il modello delle reti Bayesiane (BN). In questo modo, è possibile utilizzare queste informazioni provenienti da diversi livelli per ottimizzare le prestazioni della rete, utilizzando un approccio di tipo cross-layer. Ad esempio, queste informazioni sono utilizzate per predire il throughput a livello di trasporto in una rete wireless di tipo single-hop, o per prevedere il verificarsi di eventi di congestione in una rete wireless di tipo multi-hop. L'approccio seguito nei due argomenti principali che compongono questa tesi è il seguente: (i) viene applicato il paradigma cognitive per ricostruire specifiche caratteristiche probabilistiche della rete, (ii) queste informazioni vengono utilizzate per progettare nuove tecniche protocollari, (iii) queste tecniche vengono analizzate teoricamente e confrontate con altre tecniche esistenti, e (iv) le prestazioni vengono simulate, confrontate con quelle di altre tecniche e valutate in scenari di rete realistici.
Книги з теми "Bayesian recovery"
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.
Повний текст джерелаЧастини книг з теми "Bayesian recovery"
Kosarev, E. L. "Superresolution limit for Signal recovery." In Maximum Entropy and Bayesian Methods, 475–80. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-7860-8_50.
Повний текст джерелаGrant, A. I., and K. J. Packer. "Enhanced Information Recovery From Spectroscopic Data Using MaxEnt." In Maximum Entropy and Bayesian Methods, 251–59. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-015-7860-8_24.
Повний текст джерелаYang, Haiyan, Xiaolin Huang, Cheng Peng, Jie Yang, and Li Li. "A New Bayesian Method for Jointly Sparse Signal Recovery." In Neural Information Processing, 886–94. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70093-9_94.
Повний текст джерелаZhou, 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, 119–37. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6263-1_6.
Повний текст джерелаMolina, 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, 1–34. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-6514-4_1.
Повний текст джерелаChu, 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, 51–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24265-7_5.
Повний текст джерелаHuntbatch, 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, 79–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85990-1_10.
Повний текст джерелаFu, 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, 16–30. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08979-9_2.
Повний текст джерелаWang, Lu, Lifan Zhao, Guoan Bi, and Xin Liu. "Alternative Extended Block Sparse Bayesian Learning for Cluster Structured Sparse Signal Recovery." In Wireless and Satellite Systems, 3–12. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19153-5_1.
Повний текст джерелаPang, Tongyao, Yuhui Quan, and Hui Ji. "Self-supervised Bayesian Deep Learning for Image Recovery with Applications to Compressive Sensing." In Computer Vision – ECCV 2020, 475–91. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58621-8_28.
Повний текст джерелаТези доповідей конференцій з теми "Bayesian recovery"
Niinimäki, Kati, Ville Kolehmainen, and Samuli Siltanen. "Bayesian Multiresolution Method for Local Tomography." In Signal Recovery and Synthesis. Washington, D.C.: OSA, 2009. http://dx.doi.org/10.1364/srs.2009.stua2.
Повний текст джерелаJalobeanu, 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.
Повний текст джерелаPascazio, V., P. Mathieu, and G. Schirinzi. "A bayesian technique for In-SAR phase unwrapping." In Signal Recovery and Synthesis. Washington, D.C.: OSA, 2001. http://dx.doi.org/10.1364/srs.2001.smd3.
Повний текст джерелаBaskaran, Shyamsunder, and R. P. Millane. "Bayesian Image Reconstruction in X-ray Fiber Diffraction." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1998. http://dx.doi.org/10.1364/srs.1998.swa.3.
Повний текст джерелаDoerschuk, Peter C. "X-ray Crystallography as a Bayesian Signal Reconstruction Problem." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/srs.1992.tuc1.
Повний текст джерелаWu, Chi-hsin, and Peter C. Doerschuk. "Markov random fields as a priori information for image restoration." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/srs.1995.rwc2.
Повний текст джерелаMaqbool, O., and H. A. Babri. "Bayesian Learning for Software Architecture Recovery." In 2007 International Conference on Electrical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icee.2007.4287309.
Повний текст джерелаOh, 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. Washington, D.C.: OSA, 2001. http://dx.doi.org/10.1364/srs.2001.stua2.
Повний текст джерелаChen, Chin-Tu, Valen E. Johnson, Wing H. Wong, Xiaoping Hub, and Charles E. Metz. "Statistical Methods for Image Restoration and Image Reconstruction." In Signal Recovery and Synthesis. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/srs.1989.wd1.
Повний текст джерелаZerafat, Mohammad Mehdi, Shahabbodin Ayatollahi, Nasir Mehranbod, and Davaood Barzegari. "Bayesian Network Analysis as a Tool for Efficient EOR Screening." In SPE Enhanced Oil Recovery Conference. Society of Petroleum Engineers, 2011. http://dx.doi.org/10.2118/143282-ms.
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