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ISSN 打印: 2152-5080

ISSN 在线: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

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WASSERSTEIN METRIC-DRIVEN BAYESIAN INVERSION WITH APPLICATIONS TO SIGNAL PROCESSING

卷 9, 册 4, 2019, pp. 395-414
DOI: 10.1615/Int.J.UncertaintyQuantification.2019027745
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摘要

We present a Bayesian framework based on a new exponential likelihood function driven by the quadratic Wasserstein metric. Compared to conventional Bayesian models based on Gaussian likelihood functions driven by the least-squares norm (L2 norm), the new framework features several advantages. First, the new framework does not rely on the like-lihood of the measurement noise and hence can treat complicated noise structures such as combined additive and multiplicative noise. Second, unlike the normal likelihood function, the Wasserstein-based exponential likelihood function does not usually generate multiple local extrema. As a result, the new framework features better convergence to correct posteriors when a Markov Chain Monte Carlo sampling algorithm is employed. Third, in the particular case of signal processing problems, although a normal likelihood function measures only the amplitude differences between the observed and simulated signals, the new likelihood function can capture both amplitude and phase differences. We apply the new framework to a class of signal processing problems, that is, the inverse uncertainty quantification of waveforms, and demonstrate its advantages compared to Bayesian models with normal likelihood functions.

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对本文的引用
  1. Chu Ning, Hou Yaochun, Wu Dazhuan, Djafari Ali Mohammed, A Variational Bayesian Inference with Small Dataset for High-Precision Infrared Thermal Imaging, 2019 6th International Conference on Systems and Informatics (ICSAI), 2019. Crossref

  2. Dunlop Matt, Yang Yunan, New likelihood functions and level-set prior for Bayesian full-waveform inversion, SEG Technical Program Expanded Abstracts 2020, 2020. Crossref

  3. Latz Jonas, Madrigal-Cianci Juan P., Nobile Fabio, Tempone Raúl, Generalized parallel tempering on Bayesian inverse problems, Statistics and Computing, 31, 5, 2021. Crossref

  4. Dunlop Matthew M., Yang Yunan, Stability of Gibbs Posteriors from the Wasserstein Loss for Bayesian Full Waveform Inversion, SIAM/ASA Journal on Uncertainty Quantification, 9, 4, 2021. Crossref

  5. Scarinci Andrea, Marzouk Youssef, Gu Chen, Fehler Michael, Waheed Umair bin, Kaka Sanlinn, Dia Ben M., Transport Lagrangian misfit measures and velocity model uncertainty in Bayesian moment tensor inversion, First International Meeting for Applied Geoscience & Energy Expanded Abstracts, 2021. Crossref

  6. Tamang Sagar K., Ebtehaj Ardeshir, Zou Dongmian, Lerman Gilad, Regularized variational data assimilation for bias treatment using the Wasserstein metric, Quarterly Journal of the Royal Meteorological Society, 146, 730, 2020. Crossref

  7. Motamed Mohammad, A hierarchically low-rank optimal transport dissimilarity measure for structured data, BIT Numerical Mathematics, 2022. Crossref

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