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Variational Bayesian Algorithms for Maneuvering Target Tracking with Nonlinear Measurements in Sensor Networks. [PDF]
Entropy (Basel), 2023 The variational Bayesian method solves nonlinear estimation problems by iteratively computing the integral of the marginal density. Many researchers have demonstrated the fact its performance depends on the linear approximation in the computation of the ...
Hu Y +5 more
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A Geometric Variational Approach to Bayesian Inference [PDF]
Journal of the American Statistical Association, 2019 We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher-Rao metric on the manifold of probability density functions.
Abhijoy Saha +28 more
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Variational Bayesian Compressive Sensing with Equivalent Source Modeling for Sound Field Reconstruction [PDF]
Sensors (Basel)While conventional Bayesian compressive sensing exploits signal sparsity for accurate sound field reconstruction from under-sampled measurements, its practicality is limited by high computational complexity and slow convergence.
Xiao Y, Chen Z, Zhang H, Zhong C.
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Stochastic Control for Bayesian Neural Network Training
Entropy, 2022 In this paper, we propose to leverage the Bayesian uncertainty information encoded in parameter distributions to inform the learning procedure for Bayesian models.
Ludwig Winkler +2 more
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Channel estimation using variational Bayesian learning for multi‐user mmWave MIMO systems
IET Communications, 2021 This paper presents a novel variational Bayesian learning‐based channel estimation scheme for hybrid pre‐coding‐employed wideband multiuser millimetre wave multiple‐input multiple‐output communication systems.
Bo Xiao +4 more
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A primer on Variational Laplace (VL)
NeuroImage, 2023 This article details a scheme for approximate Bayesian inference, which has underpinned thousands of neuroimaging studies since its introduction 15 years ago.
Peter Zeidman, Karl Friston, Thomas Parr
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Variational Bayesian Inference in High-Dimensional Linear Mixed Models
Mathematics, 2022 In high-dimensional regression models, the Bayesian lasso with the Gaussian spike and slab priors is widely adopted to select variables and estimate unknown parameters. However, it involves large matrix computations in a standard Gibbs sampler.
Jieyi Yi, Niansheng Tang
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Variational Bayesian Unlearning
CoRR, 2020 This paper studies the problem of approximately unlearning a Bayesian model from a small subset of the training data to be erased. We frame this problem as one of minimizing the Kullback-Leibler divergence between the approximate posterior belief of model parameters after directly unlearning from erased data vs.
Quoc Phong Nguyen +2 more
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Adaptive Non-Rigid Point Set Registration Based on Variational Bayesian [PDF]
Xibei Gongye Daxue Xuebao, 2018 For the existence of outliers in non-rigid point set registration, a method based on Bayesian student's t mixture model(SMM) is proposed. Under the framework of variational Bayesian, the point set registration problem is converted to maximize the ...
doaj +1 more source
Variational Bayesian Super Resolution [PDF]
IEEE Transactions on Image Processing, 2011 In this paper, we address the super resolution (SR) problem from a set of degraded low resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the sub-pixel motion between the LR images significantly affects the performance of the reconstructed HR image. In this paper, we propose novel super resolution methods where the HR
S. Derin Babacan +2 more
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