Results 31 to 40 of about 43,983 (273)
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 inference for fMRI time series [PDF]
NeuroImage, 2003 We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models with Autoregressive (AR) error processes. We make use of the Variational Bayesian (VB) framework which approximates the true posterior density with a factorised density.
William D. Penny +2 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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Fast Variational Bayesian Inference for Space-Time Adaptive Processing
Remote Sensing, 2023 Space-time adaptive processing (STAP) approaches based on sparse Bayesian learning (SBL) have attracted much attention for the benefit of reducing the training samples requirement and accurately recovering sparse signals. However, it has the problem of a
Xinying Zhang, Tong Wang, Degen Wang
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A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms [PDF]
, 2018 The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine learning community.
Cox, Marco +2 more
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Scientific Reports, 2022 In this paper, the inverse problems of cardiac sources using analytical and probabilistic methods are solved and discussed. The standard Tikhonov regularization technique is solved initially to estimate the under-determined heart surface potentials from ...
Vikas R. Bhat +3 more
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Memorized Variational Continual Learning for Dirichlet Process Mixtures
IEEE Access, 2019 Bayesian nonparametric models are theoretically suitable for streaming data due to their ability to adapt model complexity with the observed data. However, very limited work has addressed posterior inference in a streaming fashion, and most of the ...
Yang Yang, Bo Chen, Hongwei Liu
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VIP - Variational Inversion Package with example implementations of Bayesian tomographic imaging
SeismicaBayesian inference has become an important methodology to solve inverse problems and to quantify uncertainties in their solutions. Variational inference is a method that provides probabilistic, Bayesian solutions efficiently by using optimisation.
Xin Zhang, Andrew Curtis
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VIGoR: Variational Bayesian Inference for Genome-Wide Regression
Journal of Open Research Software, 2016 Genome-wide regression using a number of genome-wide markers as predictors is now widely used for genome-wide association mapping and genomic prediction.
Akio Onogi, Hiroyoshi Iwata
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A Variational View on Bootstrap Ensembles as Bayesian Inference
CoRR, 2020 In this paper, we employ variational arguments to establish a connection between ensemble methods for Neural Networks and Bayesian inference. We consider an ensemble-based scheme where each model/particle corresponds to a perturbation of the data by means of parametric bootstrap and a perturbation of the prior.
Dimitrios Milios +2 more
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