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Статті в журналах з теми "Bayesian recovery"

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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

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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Анотація:

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 algorithm is proposed via variational Bayesian inference. Moreover, we present a fast version of the proposed recovery algorithm, which does not involve the computation of matrix inversion and has robust recovery performance in the low SNR case. The experimental results with simulated data and ISAR imaging show that the proposed algorithms can efficiently reconstruct BSS and have good antinoise ability in noisy environments.

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2

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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3

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 (November 14, 2011): 2640–46. http://dx.doi.org/10.3724/sp.j.1146.2011.00151.

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4

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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5

Korki, 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.

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6

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 (September 2000): 951–56. http://dx.doi.org/10.1111/j.0006-341x.2000.00951.x.

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7

Wang, 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.

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8

Huang, 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.

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9

Ahmed, 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.

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10

Zhang, 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.

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11

LI, Jia. "Joint Bayesian and Greedy Recovery for Compressive Sensing." Chinese Journal of Electronics 29, no. 5 (September 1, 2020): 945–51. http://dx.doi.org/10.1049/cje.2020.08.010.

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12

Sun, Shouwang, Sheng Jiao, Qi Hu, Zhiwen Wang, Zili Xia, Youliang Ding, and Letian Yi. "Missing Structural Health Monitoring Data Recovery Based on Bayesian Matrix Factorization." Sustainability 15, no. 4 (February 6, 2023): 2951. http://dx.doi.org/10.3390/su15042951.

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Анотація:

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 monitoring data from a single sensor at a time, so the spatiotemporal dependence among monitoring data from different sensors cannot be extracted to recover missing data. Monitoring data from multiple sensors can be organized in the form of matrix. Therefore, matrix factorization is an appropriate way to handle monitoring data. To this end, a hierarchical probabilistic model for matrix factorization is formulated under a fully Bayesian framework by incorporating a sparsity-inducing prior over spatiotemporal factors. The spatiotemporal dependence is modeled to reconstruct the monitoring data matrix to achieve the missing-data recovery. Through experiments using continuous monitoring data of an in-service bridge, the proposed method shows good performance of missing-data recovery. Furthermore, the effect of missing data on the preset rank of matrix is also investigated. The results show that the model can achieve higher accuracy of missing-data recovery with higher preset rank under the same case of missing data.

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13

Zhanjun, Hao, Li Beibei, and Dang Xiaochao. "A Signal Recovery Method Based on Bayesian Compressive Sensing." Mathematical Problems in Engineering 2019 (February 11, 2019): 1–13. http://dx.doi.org/10.1155/2019/7235239.

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In a precise positioning system, weak signal errors caused by the influence of a human body on signal transmission in complex environments are a main cause of the reduced reliability of communication and positioning accuracy. Therefore, eliminating the influence of interference from human crawling waves on signal transmissions in complex environments is an important task in improving positioning systems. To conclude, an experimental environment is designed in this paper and a method using the Ultra-Wideband (UWB) Local Positioning System II (UWB LPS), called Bayesian Compressed Sensing-Crawling Waves (BCS-CW), is proposed to eliminate the impact of crawling waves using Bayesian compressive sensing. First, analyse the transmission law for crawling waves on the human body. Second, Bayesian compressive sensing is used to recover the UWB crawling wave signal. Then, the algorithm is combined with the maximum likelihood estimation and iterative approximation algorithms to determine the label position. Finally, through experimental verification, the positioning accuracy of this method is shown to be greatly improved compared to that of other algorithms.

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14

Routtenberg, Tirza. "Non-Bayesian Estimation Framework for Signal Recovery on Graphs." IEEE Transactions on Signal Processing 69 (2021): 1169–84. http://dx.doi.org/10.1109/tsp.2021.3054995.

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15

Rajeshwari, T., and C. Thangamani. "Attack Impact Discovery and Recovery with Dynamic Bayesian Networks." Asian Journal of Computer Science and Technology 8, S1 (February 5, 2019): 74–79. http://dx.doi.org/10.51983/ajcst-2019.8.s1.1953.

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The network attacks are discovered using the Intrusion Detection Systems (IDS). Anomaly, signature and compound attack detection schemes are employed to fetch malicious data traffic activities. The attack impact analysis operations are carried out to discover the malicious objects in the network. The system objects are contaminated with process injection or hijacking. The attack ramification model discovers the contaminated objects. The dependency networks are built to model the information flow over the objects in the network. The dependency network is a directed graph built to indicate the data communication over the objects. The attack ramification models are designed with intrusion root information. The attack ramifications are applied to identify the malicious objects and contaminated objects. The attack ramifications are discovered with the information flows from the attack sources. The Attack Ramification with Bayesian Network (ARBN) scheme discovers the attack impact without the knowledge of the intrusion root. The probabilistic reasoning approach is employed to analyze the object state for ramification process. The objects lifetime is divided into temporal slices to verify the object state changes. The system call traces and object slices are correlated to construct the Temporal Dependency Network (TDN). The Bayesian Network (BN) is constructed with the uncertain data communication activities extracted from the TDN. The attack impact is fetched with loopy belief propagation on the BN model. The network security system is built with attack impact analysis and recovery operations. Live traffic data analysis process is carried out with improved temporal slicing concepts. Attack Ramification and Recovery with Dynamic Bayesian Network (ARRDBN) is built to support attack impact analysis and recovery tasks. The unsupervised attack handling mechanism automatically discovers the feasible solution for the associated attacks.

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16

Almond, Russell, Duanli Yan, and Lisa Hemat. "Parameter Recovery Studies With a Diagnostic Bayesian Network Model." Behaviormetrika 35, no. 2 (July 2008): 159–85. http://dx.doi.org/10.2333/bhmk.35.159.

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17

Giri, Ritwik, and Bhaskar Rao. "Learning Distributional Parameters for Adaptive Bayesian Sparse Signal Recovery." IEEE Computational Intelligence Magazine 11, no. 4 (November 2016): 14–23. http://dx.doi.org/10.1109/mci.2016.2601700.

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18

Parlikad, Ajith Kumar, and Duncan McFarlane. "A Bayesian decision support system for vehicle component recovery." International Journal of Sustainable Manufacturing 1, no. 4 (2009): 415. http://dx.doi.org/10.1504/ijsm.2009.031362.

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19

Wang, Lu, Lifan Zhao, Lei Yu, Jingjing Wang, and Guoan Bi. "Structured Bayesian learning for recovery of clustered sparse signal." Signal Processing 166 (January 2020): 107255. http://dx.doi.org/10.1016/j.sigpro.2019.107255.

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20

Razi, Abolfazl. "Bayesian Signal Recovery Under Measurement Matrix Uncertainty: Performance Analysis." IEEE Access 7 (2019): 102356–65. http://dx.doi.org/10.1109/access.2019.2930236.

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21

Gong, Ting. "Bayesian sparse signal recovery based on log-Laplacian prior." Journal of Applied Remote Sensing 12, no. 04 (October 10, 2018): 1. http://dx.doi.org/10.1117/1.jrs.12.045003.

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22

Engemann, Kristie M., and Michael T. Owyang. "WHATEVER HAPPENED TO THE BUSINESS CYCLE? A BAYESIAN ANALYSIS OF JOBLESS RECOVERIES." Macroeconomic Dynamics 14, no. 5 (July 30, 2010): 709–26. http://dx.doi.org/10.1017/s1365100509990812.

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Анотація:

During the typical recovery from U.S. postwar period economic downturns, employment recovers to its prerecession level within months of the output trough. However, during the past two recoveries, employment has taken up to three years to achieve its prerecession benchmark. We propose a formal empirical model of business cycles with recovery periods to demonstrate that the past two recoveries have been statistically different from previous experiences. We find that this difference can be attributed to a shift in the speed of transition between business cycle regimes. Moreover, we find this shift results from both durable and nondurable manufacturing sectors losing their cyclical characteristics. We argue that this finding of acyclicality in post-1980 manufacturing sectors is consistent with previous hypotheses (e.g., improved inventory management) regarding the reduction in macroeconomic volatility over the same period. These results suggest a link between the two phenomena, which have heretofore been studied separately.

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23

Adams, Jadie, Steven Lu, Krzysztof M. Gorski, Graca Rocha, and Kiri L. Wagstaff. "Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 15640–46. http://dx.doi.org/10.1609/aaai.v37i13.26854.

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The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe. However, observations of the CMB are contaminated by foreground emissions, obscuring the CMB signal and reducing its efficacy in constraining cosmological parameters. We employ deep learning as a data-driven approach to CMB cleaning from multi-frequency full-sky maps. In particular, we develop a graph-based Bayesian convolutional neural network based on the U-Net architecture that predicts cleaned CMB with pixel-wise uncertainty estimates. We demonstrate the potential of this technique on realistic simulated data based on the Planck mission. We show that our model ac- accurately recovers the cleaned CMB sky map and resulting angular power spectrum while identifying regions of uncertainty. Finally, we discuss the current challenges and the path forward for deploying our model for CMB recovery on real observations.

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24

Bonkhoff, Anna K., Thomas Hope, Danilo Bzdok, Adrian G. Guggisberg, Rachel L. Hawe, Sean P. Dukelow, Anne K. Rehme, Gereon R. Fink, Christian Grefkes, and Howard Bowman. "Bringing proportional recovery into proportion: Bayesian modelling of post-stroke motor impairment." Brain 143, no. 7 (June 29, 2020): 2189–206. http://dx.doi.org/10.1093/brain/awaa146.

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Abstract Accurate predictions of motor impairment after stroke are of cardinal importance for the patient, clinician, and healthcare system. More than 10 years ago, the proportional recovery rule was introduced by promising that high-fidelity predictions of recovery following stroke were based only on the initially lost motor function, at least for a specific fraction of patients. However, emerging evidence suggests that this recovery rule is subject to various confounds and may apply less universally than previously assumed. Here, we systematically revisited stroke outcome predictions by applying strategies to avoid confounds and fitting hierarchical Bayesian models. We jointly analysed 385 post-stroke trajectories from six separate studies—one of the largest overall datasets of upper limb motor recovery. We addressed confounding ceiling effects by introducing a subset approach and ensured correct model estimation through synthetic data simulations. Subsequently, we used model comparisons to assess the underlying nature of recovery within our empirical recovery data. The first model comparison, relying on the conventional fraction of patients called ‘fitters’, pointed to a combination of proportional to lost function and constant recovery. ‘Proportional to lost’ here describes the original notion of proportionality, indicating greater recovery in case of a more severe initial impairment. This combination explained only 32% of the variance in recovery, which is in stark contrast to previous reports of >80%. When instead analysing the complete spectrum of subjects, ‘fitters’ and ‘non-fitters’, a combination of proportional to spared function and constant recovery was favoured, implying a more significant improvement in case of more preserved function. Explained variance was at 53%. Therefore, our quantitative findings suggest that motor recovery post-stroke may exhibit some characteristics of proportionality. However, the variance explained was substantially reduced compared to what has previously been reported. This finding motivates future research moving beyond solely behaviour scores to explain stroke recovery and establish robust and discriminating single-subject predictions.

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25

Yi, Ming, Meng Wang, Evangelos Farantatos, and Tapas Barik. "Bayesian robust hankel matrix completion with uncertainty modeling for synchrophasor data recovery." ACM SIGEnergy Energy Informatics Review 2, no. 1 (February 2022): 1–19. http://dx.doi.org/10.1145/3527579.3527580.

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Synchrophasor data suffer from quality issues like missing and bad data. Exploiting the low-rankness of the Hankel matrix of the synchrophasor data, this paper formulates the data recovery problem as a robust low-rank Hankel matrix completion problem and proposes a Bayesian data recovery method that estimates the posterior distribution of synchrophasor data from partial observations. In contrast to the deterministic approaches, our proposed Bayesian method provides an uncertainty index to evaluate the confidence of each estimation. To the best of our knowledge, this is the first method that provides confidence measure for synchrophasor data recovery. Numerical experiments on synthetic data and recorded synchrophasor data demonstrate that our method outperforms existing low-rank matrix completion methods.

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26

Shekaramiz, Mohammad, and Todd K. Moon. "Compressive Sensing via Variational Bayesian Inference under Two Widely Used Priors: Modeling, Comparison and Discussion." Entropy 25, no. 3 (March 16, 2023): 511. http://dx.doi.org/10.3390/e25030511.

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Анотація:

Compressive sensing is a sub-Nyquist sampling technique for efficient signal acquisition and reconstruction of sparse or compressible signals. In order to account for the sparsity of the underlying signal of interest, it is common to use sparsifying priors such as Bernoulli–Gaussian-inverse Gamma (BGiG) and Gaussian-inverse Gamma (GiG) priors on the components of the signal. With the introduction of variational Bayesian inference, the sparse Bayesian learning (SBL) methods for solving the inverse problem of compressive sensing have received significant interest as the SBL methods become more efficient in terms of execution time. In this paper, we consider the sparse signal recovery problem using compressive sensing and the variational Bayesian (VB) inference framework. More specifically, we consider two widely used Bayesian models of BGiG and GiG for modeling the underlying sparse signal for this problem. Although these two models have been widely used for sparse recovery problems under various signal structures, the question of which model can outperform the other for sparse signal recovery under no specific structure has yet to be fully addressed under the VB inference setting. Here, we study these two models specifically under VB inference in detail, provide some motivating examples regarding the issues in signal reconstruction that may occur under each model, perform comparisons and provide suggestions on how to improve the performance of each model.

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27

Budiana, Stevanny, Felivia Kusnadi, and Robyn Irawan. "BAYESIAN ADDITIVE REGRESSION TREE APPLICATION FOR PREDICTING MATERNITY RECOVERY RATE OF GROUP LONG-TERM DISABILITY INSURANCE." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 1 (April 15, 2023): 0135–46. http://dx.doi.org/10.30598/barekengvol17iss1pp0135-0146.

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Bayesian Additive Regression Tree (BART) is a sum-of-trees model used to approximate classification or regression cases. The main idea of this method is to use a prior distribution to keep the tree size small and a likelihood from data to get the posterior. By fixing the tree size as small as possible, the approximation of each tree would have a little effect on the posterior, which is the sum of all output from all the trees used. Bayesian additive regression tree method will be used for predicting the maternity recovery rate of group long-term disability insurance data from the Society of Actuaries (SOA). The decision tree-based models such as Gradient Boosting Machine, Random Forest, Decision Tree, and Bayesian Additive Regression Tree model are compared to find the best model by comparing mean squared error and program runtime. After comparing some models, the Bayesian Additive Regression Tree model gives the best prediction based on smaller root mean squared error values and relatively short runtime.

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28

Li, Junlin, Wei Zhou, and Cheng Cheng. "Adaptive support-driven Bayesian reweighted algorithm for sparse signal recovery." Signal, Image and Video Processing 15, no. 6 (January 31, 2021): 1295–302. http://dx.doi.org/10.1007/s11760-021-01860-2.

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29

SONG, Jinyang, Feng SHEN, Xiaobo CHEN, and Di ZHAO. "Robust Sparse Signal Recovery in Impulsive Noise Using Bayesian Methods." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E101.A, no. 1 (2018): 273–78. http://dx.doi.org/10.1587/transfun.e101.a.273.

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30

Dunham, Kylee D., Erik E. Osnas, Charles J. Frost, Julian B. Fischer, and James B. Grand. "Assessing recovery of spectacled eiders using a Bayesian decision analysis." PLOS ONE 16, no. 7 (July 1, 2021): e0253895. http://dx.doi.org/10.1371/journal.pone.0253895.

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Assessing species status and making classification decisions under the Endangered Species Act is a critical step towards effective species conservation. However, classification decisions are liable to two errors: i) failing to classify a species as threatened or endangered that should be classified (underprotection), or ii) classifying a species as threatened or endangered when it is not warranted (overprotection). Recent surveys indicate threatened spectacled eider populations are increasing in western Alaska, prompting the U.S. Fish and Wildlife Service to reconsider the federal listing status. There are multiple criteria set for assessing spectacled eider status, and here we focus on the abundance and decision analysis criteria. We estimated population metrics using state-space models for Alaskan breeding populations of spectacled eiders. We projected abundance over 50 years using posterior estimates of abundance and process variation to estimate the probability of quasi-extinction. The decision analysis maps the risk of quasi-extinction to the loss associated with making a misclassification error (i.e., underprotection) through a loss function. Our results indicate that the Yukon Kuskokwim Delta breeding population in western Alaska has met the recovery criteria but the Arctic Coastal Plain population in northern Alaska has not. The methods employed here provide an example of accounting for uncertainty and incorporating value judgements in such a way that the decision-makers may understand the risk of committing a misclassification error. Incorporating the abundance threshold and decision analysis in the reclassification criteria greatly increases the transparency and defensibility of the classification decision, a critical aspect for making effective decisions about species management and conservation.

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31

Jiao, Libin, Hao Wu, Haodi Wang, and Rongfang Bie. "Text Recovery via Deep CNN-BiLSTM Recognition and Bayesian Inference." IEEE Access 6 (2018): 76416–28. http://dx.doi.org/10.1109/access.2018.2882592.

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32

Knapik, B. T., A. W. van der Vaart, and J. H. van Zanten. "Bayesian Recovery of the Initial Condition for the Heat Equation." Communications in Statistics - Theory and Methods 42, no. 7 (April 2013): 1294–313. http://dx.doi.org/10.1080/03610926.2012.681417.

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33

Qian, W., and D. M. Titterington. "Bayesian image restoration: an application to edge-preserving surface recovery." IEEE Transactions on Pattern Analysis and Machine Intelligence 15, no. 7 (July 1993): 748–52. http://dx.doi.org/10.1109/34.221174.

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34

Cevri, M., and D. Üstündağ. "Bayesian recovery of sinusoids from noisy data with parallel tempering." IET Signal Processing 6, no. 7 (2012): 673. http://dx.doi.org/10.1049/iet-spr.2011.0335.

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35

Ali, Anum, Mudassir Masood, Muhammad S. Sohail, Samir N. Al-Ghadhban, and Tareq Y. Al-Naffouri. "Narrowband Interference Mitigation in SC-FDMA Using Bayesian Sparse Recovery." IEEE Transactions on Signal Processing 64, no. 24 (December 15, 2016): 6471–84. http://dx.doi.org/10.1109/tsp.2016.2614484.

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36

Ortega-Argueta, Alejandro. "Improving recovery planning for threatened species through Bayesian belief networks." Biological Conservation 241 (January 2020): 108320. http://dx.doi.org/10.1016/j.biocon.2019.108320.

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37

Khanna, Saurabh, and Chandra R. Murthy. "Decentralized Joint-Sparse Signal Recovery: A Sparse Bayesian Learning Approach." IEEE Transactions on Signal and Information Processing over Networks 3, no. 1 (March 2017): 29–45. http://dx.doi.org/10.1109/tsipn.2016.2612120.

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38

Parlikad, Ajith Kumar, and Duncan McFarlane. "Value of information in product recovery decisions: a Bayesian approach." International Journal of Sustainable Engineering 3, no. 2 (June 2010): 106–20. http://dx.doi.org/10.1080/19397030903499810.

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39

Wang, Dan, and Zhuhong Zhang. "Variational Bayesian inference based robust multiple measurement sparse signal recovery." Digital Signal Processing 89 (June 2019): 131–44. http://dx.doi.org/10.1016/j.dsp.2019.03.013.

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40

Widarsson, Björn, and Erik Dotzauer. "Bayesian network-based early-warning for leakage in recovery boilers." Applied Thermal Engineering 28, no. 7 (May 2008): 754–60. http://dx.doi.org/10.1016/j.applthermaleng.2007.06.016.

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41

Benazzouza, Salma, Mohammed Ridouani, Fatima Salahdine, and Aawatif Hayar. "Chaotic Compressive Spectrum Sensing Based on Chebyshev Map for Cognitive Radio Networks." Symmetry 13, no. 3 (March 7, 2021): 429. http://dx.doi.org/10.3390/sym13030429.

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Анотація:

Recently, the chaotic compressive sensing paradigm has been widely used in many areas, due to its ability to reduce data acquisition time with high security. For cognitive radio networks (CRNs), this mechanism aims at detecting the spectrum holes based on few measurements taken from the original sparse signal. To ensure a high performance of the acquisition and recovery process, the choice of a suitable sensing matrix and the appropriate recovery algorithm should be done carefully. In this paper, a new chaotic compressive spectrum sensing (CSS) solution is proposed for cooperative CRNs based on the Chebyshev sensing matrix and the Bayesian recovery via Laplace prior. The chaotic sensing matrix is used first to acquire and compress the high-dimensional signal, which can be an interesting topic to be published in symmetry journal, especially in the data-compression subsection. Moreover, this type of matrix provides reliable and secure spectrum detection as opposed to random sensing matrix, since any small change in the initial parameters generates a different sensing matrix. For the recovery process, unlike the convex and greedy algorithms, Bayesian models are fast, require less measurement, and deal with uncertainty. Numerical simulations prove that the proposed combination is highly efficient, since the Bayesian algorithm with the Chebyshev sensing matrix provides superior performances, with compressive measurements. Technically, this number can be reduced to 20% of the length and still provides a substantial performance.

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Magris, G., C. Mateu, G. Bruzual A., and I. Cabrera. "On the Recovery of Galaxy Properties from Spectral Fits." Proceedings of the International Astronomical Union 8, S295 (August 2012): 317. http://dx.doi.org/10.1017/s174392131300519x.

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AbstractWe show the results of a non-parametric, fully bayesian implementation of a spectral fitting algorithm, designed to calculate the main physical parameters that govern the galaxy assembly process. In this work, we present results from a statistical treatment of SED fitting that allows for easy recovery and visualization of the galaxy physical parameters.

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43

Reichenberg, Ray E., Roy Levy, and Adam Clark. "Considerations for Fitting Dynamic Bayesian Networks With Latent Variables: A Monte Carlo Study." Applied Psychological Measurement 46, no. 2 (February 10, 2022): 116–35. http://dx.doi.org/10.1177/01466216211066609.

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Dynamic Bayesian networks (DBNs; Reye, 2004) are a promising tool for modeling student proficiency under rich measurement scenarios (Reichenberg, 2018). These scenarios often present assessment conditions far more complex than what is seen with more traditional assessments and require assessment arguments and psychometric models capable of integrating those complexities. Unfortunately, DBNs remain understudied and their psychometric properties relatively unknown. The current work aimed at exploring the properties of DBNs under a variety of realistic psychometric conditions. A Monte Carlo simulation study was conducted in order to evaluate parameter recovery for DBNs using maximum likelihood estimation. Manipulated factors included sample size, measurement quality, test length, the number of measurement occasions. Results suggested that measurement quality has the most prominent impact on estimation quality with more distinct performance categories yielding better estimation. From a practical perspective, parameter recovery appeared to be sufficient with samples as low as N = 400 as long as measurement quality was not poor and at least three items were present at each measurement occasion. Tests consisting of only a single item required exceptional measurement quality in order to adequately recover model parameters.

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Bonkhoff, Anna K., Tom Hope, Danilo Bzdok, Adrian G. Guggisberg, Rachel L. Hawe, Sean P. Dukelow, François Chollet, David J. Lin, Christian Grefkes, and Howard Bowman. "Recovery after stroke: the severely impaired are a distinct group." Journal of Neurology, Neurosurgery & Psychiatry 93, no. 4 (December 22, 2021): 369–78. http://dx.doi.org/10.1136/jnnp-2021-327211.

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IntroductionStroke causes different levels of impairment and the degree of recovery varies greatly between patients. The majority of recovery studies are biased towards patients with mild-to-moderate impairments, challenging a unified recovery process framework. Our aim was to develop a statistical framework to analyse recovery patterns in patients with severe and non-severe initial impairment and concurrently investigate whether they recovered differently.MethodsWe designed a Bayesian hierarchical model to estimate 3–6 months upper limb Fugl-Meyer (FM) scores after stroke. When focusing on the explanation of recovery patterns, we addressed confounds affecting previous recovery studies and considered patients with FM-initial scores <45 only. We systematically explored different FM-breakpoints between severe/non-severe patients (FM-initial=5–30). In model comparisons, we evaluated whether impairment-level-specific recovery patterns indeed existed. Finally, we estimated the out-of-sample prediction performance for patients across the entire initial impairment range.ResultsRecovery data was assembled from eight patient cohorts (n=489). Data were best modelled by incorporating two subgroups (breakpoint: FM-initial=10). Both subgroups recovered a comparable constant amount, but with different proportional components: severely affected patients recovered more the smaller their impairment, while non-severely affected patients recovered more the larger their initial impairment. Prediction of 3–6 months outcomes could be done with an R2=63.5% (95% CI=51.4% to 75.5%).ConclusionsOur work highlights the benefit of simultaneously modelling recovery of severely-to-non-severely impaired patients and demonstrates both shared and distinct recovery patterns. Our findings provide evidence that the severe/non-severe subdivision in recovery modelling is not an artefact of previous confounds. The presented out-of-sample prediction performance may serve as benchmark to evaluate promising biomarkers of stroke recovery.

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JOHANSEN, ANDERS, and DIDIER SORNETTE. "EVALUATION OF THE QUANTITATIVE PREDICTION OF A TREND REVERSAL ON THE JAPANESE STOCK MARKET IN 1999." International Journal of Modern Physics C 11, no. 02 (March 2000): 359–64. http://dx.doi.org/10.1142/s012918310000033x.

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In January 1999, the authors published a quantitative prediction that the Nikkei index should recover from its 14-year low in January 1999 and reach ≈20 500 a year later. The purpose of the present paper is to evaluate the performance of this specific prediction as well as the underlying model: the forecast, performed at a time when the Nikkei was at its lowest (as we can now judge in hindsight), has correctly captured the change of trend as well as the quantitative evolution of the Nikkei index since its inception. As the change of trend from sluggish to recovery was estimated quite unlikely by many observers at that time, a Bayesian analysis shows that a skeptical (resp. neutral) Bayesian sees prior belief in our model amplified into a posterior belief 19 times larger (resp. reach the 95% level).

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Lavielle, Marc. "2-D Bayesian deconvolution." GEOPHYSICS 56, no. 12 (December 1991): 2008–18. http://dx.doi.org/10.1190/1.1443013.

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Inverse problems can be solved in different ways. One way is to define natural criteria of good recovery and build an objective function to be minimized. If, instead, we prefer a Bayesian approach, inversion can be formulated as an estimation problem where a priori information is introduced and the a posteriori distribution of the unobserved variables is maximized. When this distribution is a Gibbs distribution, these two methods are equivalent. Furthermore, global optimization of the objective function can be performed with a Monte Carlo technique, in spite of the presence of numerous local minima. Application to multitrace deconvolution is proposed. In traditional 1-D deconvolution, a set of uni‐dimensional processes models the seismic data, while a Markov random field is used for 2-D deconvolution. In fact, the introduction of a neighborhood system permits one to model the layer structure that exists in the earth and to obtain solutions that present lateral coherency. Moreover, optimization of an appropriated objective function by simulated annealing allows one to control the fit with the input data as well as the spatial distribution of the reflectors. Extension to 3-D deconvolution is straightforward.

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Zhang, Yanbin, Long-Ting Huang, Yangqing Li, Kai Zhang, and Changchuan Yin. "Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning." Sensors 22, no. 1 (January 4, 2022): 343. http://dx.doi.org/10.3390/s22010343.

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In order to reduce the amount of hyperspectral imaging (HSI) data transmission required through hyperspectral remote sensing (HRS), we propose a structured low-rank and joint-sparse (L&S) data compression and reconstruction method. The proposed method exploits spatial and spectral correlations in HSI data using sparse Bayesian learning and compressive sensing (CS). By utilizing a simultaneously L&S data model, we employ the information of the principal components and Bayesian learning to reconstruct the hyperspectral images. The simulation results demonstrate that the proposed method is superior to LRMR and SS&LR methods in terms of reconstruction accuracy and computational burden under the same signal-to-noise tatio (SNR) and compression ratio.

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48

Kim, Taeyoung, Seungbae Choi, and Hae-Gyung Yoon. "Prelude to Machine Learning-Based IRT Research: Bayesian Item Parameter Recovery." Korean Data Analysis Society 23, no. 4 (August 30, 2021): 1509–16. http://dx.doi.org/10.37727/jkdas.2021.23.4.1509.

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Bidon, Stephanie, Marie Lasserre, and Francois Le Chevalier. "Unambiguous Sparse Recovery of Migrating Targets With a Robustified Bayesian Model." IEEE Transactions on Aerospace and Electronic Systems 55, no. 1 (February 2019): 108–23. http://dx.doi.org/10.1109/taes.2018.2848360.

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50

Wilson, Kat, Erika E. Lentz, Jennifer L. Miselis, Ilgar Safak, and Owen T. Brenner. "A Bayesian Approach to Predict Sub-Annual Beach Change and Recovery." Estuaries and Coasts 42, no. 1 (August 27, 2018): 112–31. http://dx.doi.org/10.1007/s12237-018-0444-1.

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