The Variational Bayes Method in Signal Processing (Signals and Communication Technology)
By V. Šmídl, A. Quinn. 2005
367 citations recorded by the source corpus.
How researchers see this paper
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- Jingjia Xu
- Dimitris G. Tzikas
- J. Sapp
- A. Rahimi
- Fei Gao
- M. Horacek
- N. Galatsanos
- Linwei Wang
Citations in the corpus
- Robust Transmural Electrophysiological Imaging: Integrating Sparse and Dynamic Physiological Models into ECG-Based Inference (2015, methodology): “To reduce the reliance on an ad-hoc tuning of these parameters, we assume them to be unknown with Gamma distributions (the conjugate of Gaussian distributions) [11].”
- Semi-analytical approximations to statistical moments of sigmoid and softmax mappings of normal variables (2017, background): “We also expose a few niche applications of these approximations, which mainly arise in the context of variational Bayesian inference (Beal, 2003; Daunizeau et al., 2009; Friston et al., 2007; Smídl and Quinn, 2006) on specific generative models.”
- Recursive Noise Adaptive Kalman Filtering by Variational Bayesian Approximations (2009, background): “2008348 Smidl and Quinn [19] present a theoretical framework for VB-approximations, with special emphasis on recursive Bayesian inference and signal processing applications.”
- The variational approximation for Bayesian inference (2008, methodology): “This methodology is termed variational approximation [10] and can be used to solve complex Bayesian models where the EM algorithm cannot be applied.”
- Dynamical Variational Autoencoders: A Comprehensive Review (2020, background): “, 1999), (Bishop, 2006, Chapter 10), (Šmı́dl and Quinn, 2006), (Murphy, 2012, Chapter 21) to a fundamental Deep BN architecture has led to efficient inference and training of the resulting model called a Variational Autoencoder (VAE) (Kingma and Welling, 2014).”