Results 241 to 250 of about 63,287 (274)

Variational Bayesian Method for Retinex

IEEE Transactions on Image Processing, 2014
In this paper, we propose a variational Bayesian method for Retinex to simulate and interpret how the human visual system perceives color. To construct a hierarchical Bayesian model, we use the Gibbs distributions as prior distributions for the reflectance and the illumination, and the gamma distributions for the model parameters.
Liqian Wang, Liang Xiao 0001, Hongyi Liu 0001, Zhihui Wei   +3 more
openaire   +2 more sources

The variational approximation for Bayesian inference

IEEE Signal Processing Magazine, 2008
The influence of this Thomas Bayes' work was immense. It was from here that "Bayesian" ideas first spread through the mathematical world, as Bayes's own article was ignored until 1780 and played no important role in scientific debate until the 20th century.
Dimitris G. Tzikas, Aristidis C. Likas, Nikolas P. Galatsanos   +2 more
openaire   +1 more source

Optimal transport and variational Bayesian inference

International Journal of Approximate Reasoning, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alireza Bahraini, Saeed Sadeghi
openaire   +1 more source

Variational Bayesian functional PCA

Computational Statistics & Data Analysis, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A note on variational Bayesian factor analysis

Neural Networks, 2009
Existing works on variational bayesian (VB) treatment for factor analysis (FA) model such as [Ghahramani, Z., & Beal, M. (2000). Variational inference for Bayesian mixture of factor analysers. In Advances in neural information proceeding systems. Cambridge, MA: MIT Press; Nielsen, F. B. (2004). Variational approach to factor analysis and related models.
Jianhua Zhao, Philip L. H. Yu
openaire   +5 more sources

Variational Bayesian Sparsification for Distillation Compression

2020 IEEE International Conference on Multimedia and Expo (ICME), 2020
Model compression is a critical technique for cumbersome models to reduce memory consumption and accelerate inference. Here, we propose a novel method, called Variational Bayesian Sparsification, for distilling large models into small and sparse models while maintaining accuracy.
Yue Ming 0001, Hao Fu
openaire   +1 more source

A variational approach to robust Bayesian interpolation

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718), 2004
We detail a Bayesian interpolation procedure for linear-in-the-parameter models, which combines both effective complexity control and robustness to outliers. Robustness is obtained by adopting a student-t noise distribution, defined hierarchically in terms of an inverse-gamma prior distribution over individual Gaussian observation variances ...
Michael E. Tipping, Neil D. Lawrence
openaire   +1 more source

Variational Bayesian Tensor Quantile Regression

Acta Mathematica Sinica, English Series
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jin, Yunzhi, Zhang, Yanqing
openaire   +1 more source

Variational inference based distributed noise adaptive Bayesian filter

Signal Processing, 2021
Haoshen Lin, Chen Hu
exaly  

Fast and accurate Bayesian polygenic risk modeling with variational inference

American Journal of Human Genetics, 2023
Shadi Zabad, Simon Gravel, Yue Li
exaly