Results 21 to 30 of about 63,287 (274)
Variational Bayesian Inference Techniques [PDF]
IEEE Signal Processing Magazine, 2010 Milestones in sparse signal reconstruction and compressive sensing can be understood in a probabilistic Bayesian context, fusing underdetermined measurements with knowledge about low-level signal properties in the posterior distribution, which is maximized for point estimation. We review recent progress to advance beyond this setting.
Matthias W. Seeger, David P. Wipf
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Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM [PDF]
, 2013 We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known.
Almeida +39 more
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Sparse Online Variational Bayesian Regression
SIAM/ASA Journal on Uncertainty Quantification, 2022 This work considers variational Bayesian inference as an inexpensive and scalable alternative to a fully Bayesian approach in the context of sparsity-promoting priors. In particular, the priors considered arise from scale mixtures of Normal distributions with a generalized inverse Gaussian mixing distribution.
Kody J. H. Law, Vitaly Zankin
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EURASIP Journal on Advances in Signal Processing, 2020 The paper presents a model set adaptive filtering algorithm based on variational Bayesian approximation (MSA-VB) for the target tracking system with the model and noise uncertainties.
Tianli Ma, ChaoBo Chen, Song Gao
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Variational Bayesian calibration of low-cost gas sensor systems in air quality monitoring
Measurement: Sensors, 2022 Due to the impact of air quality on health, the use of low-cost gas sensor systems in air quality monitoring has increased. The deficiencies of low-cost gas sensors such as cross-sensitivities, interferences with environmental factors, and unit-to-unit ...
Georgi Tancev, Federico Grasso Toro
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Variational Bayesian Quantization
, 2020 9 pages + detailed supplement with additional full resolution reconstructed images; ICML 2020 final camera-ready version, title changed to "Variational Bayesian Quantization" following reviewer ...
Yibo Yang, Robert Bamler, Stephan Mandt
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IEEE Access, 2020 In this paper, we study the state estimation problem with an unknown guidance parameter of the interceptor, where the guidance parameter is modeled by normal-gamma distribution.
Haoshen Lin +4 more
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IEEE Access, 2023 We present a nonparametric Bayesian hierarchical (NBH) model and develop a variational approximation (VA) algorithm for the curve fitting of the functional radiation response data.
Kwang-Woo Jung +6 more
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Variational Bayesian Dropout. [PDF]
CoRR, 2019 Variational dropout (VD) is a generalization of Gaussian dropout, which aims at inferring the posterior of network weights based on a log-uniform prior on them to learn these weights as well as dropout rate simultaneously. The log-uniform prior not only interprets the regularization capacity of Gaussian dropout in network training, but also underpins ...
Yuhang Liu 0002 +4 more
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A tutorial on variational Bayesian inference
Artificial Intelligence Review, 2011 This tutorial describes the mean-field variational Bayesian approximation to inference in graphical models, using modern machine learning terminology rather than statistical physics concepts. It begins by seeking to find an approximate mean-field distribution close to the target joint in the KL-divergence sense.
Fox, C, Roberts, S
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